How steep is the income gradient?
- ground
- plan
- build
- verify
- deliver
- done14 h ago
Finding null. Written into the program's graph and articles.
- deliver14 h agoclaude-opus-5-5
I've recorded the finding as **null** (the closest available verdict to "inconclusive") and published the article. - **Main result:** high-income countries had 885 registered cancer trials per 100,000 new cancer cases; low-income countries had 13.0. That is 68 times more, but the 95% interval runs from 33.9 to 170.8. Because the interval includes 50, the pre-registered rule says inconclusive: the gap is steep, but this run can't show it's more than 50-fold. - **Full gradient:** relative to low-income countries, high-income countries had 68.0 times the trial rate, upper-middle 18.4 times and lower-middle 4.1 times. - **Low-income data are very thin:** only 64 low-income trials in total, and 15 of the 27 low-income countries had none. - **Alternative definitions:** 7 of the 9 gave a ratio above 50. The two that count each multinational trial once instead of once per country came in below it, at 38.1 and 36.0. - **Verification:** all four checks returned FAIL, so I couldn't record "confirmed" even in principle. Oddly, the re-run values shown in the check output match the reported values exactly, and I couldn't tell why the checks fail; it may be a fault in the checker itself. Both the finding and the article say this and tell readers to treat the numbers as not independently verified. - **Next:** the article says the next run will use the WHO's multi-registry trial database, with "each trial counted once per income group" pre-registered as the main measure.
- deliver14 h agoclaude-opus-5-5
→ write_article {"title": "Cancer trials per cancer case: a 68-fold income gap, but the 50-fold test is inconclusive", "subtitle": "High-income countries host about 68 times more registered cancer
- deliver14 h agoclaude-opus-5-5
→ record_finding {"verdict": "null", "text": "Using registered ClinicalTrials.gov interventional cancer trials that started in 2015–2024, high-income countries had 885 trials per 100k incident canc
- deliver14 h agoclaude-opus-5-5
→ ToolSearch {"query": "select:mcp__bistro__record_finding,mcp__bistro__write_article", "max_results": 2}
- deliver14 h agoclaude-opus-5-5
→ Bash {"command": "cat ground.md plan.md build.md | head -300; ls", "description": "Read ground, plan, build docs"}
- verify14 h ago
Re-executed 4 claims from the program directory. trial-burden-mismatch-1-ci-decision: FAIL {"primary": {"expect": {"ci95_bootstrap": [33.895, 170.831], "decision": "inconclusive", "loo_lic_min": [59.542, "PRK"], "loo_lic_max": [93.908, "UGA"]}, "got": {"groups": {"HIC": {"n_countries": 53, ; trial-burden-mismatch-1-primary-rates: FAIL {"primary": {"expect": {"ratio_hic_lic": 68.011, "groups": {"HIC": {"rate_per_100k": 885.421, "trials": 60039, "n_countries": 53}, "UMIC": {"rate_per_100k": 239.115}, "LMIC": {"rate_per_100k": 53.683}; trial-burden-mismatch-1-sensitivities: FAIL {"sensitivities": {"expect": {"deaths_denominator": {"ratio_hic_lic": 114.861}, "worldbank_current_income": {"ratio_hic_lic": 60.896}, "single_country_trials": {"ratio_hic_lic": 38.104}, "treatment_pu; trial-burden-mismatch-1-sites-and-exclusions: FAIL {"sites": {"expect": {"breast": {"ratio_hic_lic": 82.495, "lic_trials": 7}, "cervix": {"ratio_hic_lic": 53.494, "lic_trials": 17}, "liver": {"ratio_hic_lic": 79, "lic_trials": 3}, "stomach": {"ratio_h
- build14 h agoclaude-sonnet-5-5
The hypothesis is inconclusive. The high- to low-income ratio is 68-fold, but the 95% confidence interval (33.9 to 170.8) straddles the pre-registered 50-fold threshold. I measured interventional cancer trials started 2015–2024 on ClinicalTrials.gov (48,736 trials) per 100k incident cases from GLOBOCAN 2022. Each trial counts once per country that has a site. | Income group | Trials per 100k cases | |---|---| | High | 885.4 | | Upper-middle | 239.1 | | Lower-middle | 53.7 | | Low | 13.0 | - **Low-income countries:** 15 of the 27 have no trials at all, and 5 countries account for 52 of the 64 trials. Uganda has 21 and Malawi has 11. - **Other definitions:** 7 of the 9 variants I tried give a ratio above 50. Counting each trial once per income group gives 36.0, and single-country trials only gives 38.1. Using deaths as the denominator gives 114.9. - **Cancer sites:** Breast (82.5) and liver (79.0) ratios are above 50, but each rests on 7 or fewer low-income trials. Cervix is 53.5. Stomach is undefined because there are no low-income trials. - **Excluded trials:** Taiwan (1,660 trials) and Hong Kong (574) are left out because GLOBOCAN has no matching entry for them, so the 68-fold ratio is probably conservative. Five territories with no income label are also excluded. - **Environment:** The shell's default `python` has a broken `httpx` install. I ran the script with the project venv Python at `/Users/igor/Work/me/bistro-research/backend/.venv/bin/python`. The script reproduced on a second run, and the trial count matched the API's reported total. I added CSV output to `analysis.py` and registered 4 claims covering every published number, at 2% tolerance because the counts are live. These are descriptive counts only, with no clinical inference. Files are in `/Users/igor/Work/me/bistro-research/state/runs/3`: - build.md - analysis.py - table_groups.csv - table_sensitivities.csv - table_sites.csv - table_countries.csv
- build14 h agoclaude-sonnet-5-5
→ Bash {"command": "cat > build.md <<'EOF'\n# E:trial-burden-mismatch-1: income gradient of cancer-trial density\n\n**Question (pre-registered):** How steep is the income gradient?\n**Hyp
- build14 h agoclaude-sonnet-5-5
→ register_claim {"id": "sites-and-exclusions", "statement": "Site-specific HIC/LIC ratios (keyword-matched conditions): breast 82.5 (7 LIC trials), cervix 53.5 (17), liver 79.0 (3), stomach infini
- build14 h agoclaude-sonnet-5-5
→ register_claim {"id": "sensitivities", "statement": "HIC/LIC ratio under alternative definitions: deaths denominator 114.9; World Bank current income 60.9; single-country trials 38.1; treatment-p
- build14 h agoclaude-sonnet-5-5
→ register_claim {"id": "ci-decision", "statement": "Country-level bootstrap 95% CI for the HIC/LIC ratio is 33.9 to 170.8 (straddles 50), so the >50-fold hypothesis is inconclusive; leave-one-LIC-
- build14 h agoclaude-sonnet-5-5
→ register_claim {"id": "primary-rates", "statement": "Interventional cancer trials started 2015-2024 on ClinicalTrials.gov per 100k GLOBOCAN-2022 incident cases (trial counted once per country wit
- build14 h agoclaude-sonnet-5-5
Script runs and reproduces. Now registering claims.
- build14 h agoclaude-sonnet-5-5
→ Bash {"command": "cat >> analysis.py <<'EOF'\nEOF\npython3 - <<'EOF'\ns=open('analysis.py').read()\nadd='''import csv\nwith open(\"table_groups.csv\", \"w\", newline=\"\") as f:\n w
- build14 h agoclaude-sonnet-5-5
→ Bash {"command": "PY=/Users/igor/Work/me/bistro-research/backend/.venv/bin/python; $PY -c \"import httpx;print(httpx.__version__)\" && ($PY analysis.py > out.txt 2> err.txt; echo $?; ta
- build14 h agoclaude-sonnet-5-5
→ Bash {"command": "cat err.txt | head -20; which -a python python3; pip list 2>/dev/null | grep -i -E \"httpx|charset\""}
- build14 h agoclaude-sonnet-5-5
→ Bash {"command": "python analysis.py > out.txt 2> err.txt; echo $?; tail -3 err.txt; tail -1 out.txt | head -c 6000", "timeout": 600000}
- build14 h agoclaude-sonnet-5-5
→ ToolSearch {"query": "select:mcp__bistro__register_claim", "max_results": 1}
- build14 h agoclaude-sonnet-5-5
→ Bash {"command": "ls -la; cat analysis.py 2>/dev/null; git log --oneline | head"}
- plan14 h agoclaude-opus-5-5
I've written `plan.md` and `analysis.py`. The script passes a syntax check but hasn't been run, so there are no results yet. It may not finish within the 3-minute limit: it has to fetch about 49 pages from ClinicalTrials.gov at least 1 second apart, plus however long each page of 1,000 trials takes to come back. A `plan.md` from an earlier attempt was already here. I kept its design, made three small updates so it matches the script, and left the question and hypothesis unchanged. I also probed the APIs, and each still has the shape the plan assumes (about 48,700 trials match the query). **The test in `plan.md`:** - **Trials (numerator):** ClinicalTrials.gov interventional trials matching "cancer" that started in 2015–2024. A trial counts once per country where it has a site. - **Burden (denominator):** GLOBOCAN 2022 new cancer cases (all cancers except non-melanoma skin cancer). Countries are grouped by the income labels GLOBOCAN ships with its estimates. - **Metric:** for each income group, trials divided by cases, times 100,000. Trials and cases are summed across the group's countries before dividing, so countries with zero trials stay in. The ratio tested is the high-income rate divided by the low-income rate. - **Decision rule, fixed before running:** - **Supported:** the ratio is above 50 and the lower end of its 95% confidence interval is too. - **Refuted:** the ratio and the upper end of the interval are both below 50. - **Inconclusive:** anything else. The interval comes from 2,000 resamples of countries within each group, with a fixed random seed. - **Answering "how steep":** each group's rate relative to low-income countries, with China and India also taken out of their groups because they dominate them. - **Confound checks:** nine alternative versions, reported but not allowed to override the primary decision: - deaths instead of new cases - the current World Bank income groups - each trial counted once per income group - single-country trials only - high-income group without the USA - treatment trials only - phase 3 trials only - trials started 2020–2024 - trial sites instead of trials The plan also covers small-sample checks on the low-income group (dropping one country at a time), and separate ratios for breast, cervical, liver and stomach cancer. - **Limitations:** two biases can't be fixed here and are stated as limitations. ClinicalTrials.gov under-covers non-US registries, and cancer burden is poorly measured in low-income countries. **`analysis.py`:** - Uses only the standard library and httpx, takes no arguments, and makes about 57 HTTP calls, at most 4 per second. - Its last stdout line is one JSON object with every number above. That includes the country names it couldn't match, the countries with trials but no burden estimate, and its own runtime. - GLOBOCAN's list of cancer codes returned 404, so I couldn't confirm the codes for the four sites. Instead the script prints each code's 2022 world total so they can be checked against the published figures (about 2.3M breast and 0.66M cervical cases).
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "grep -c -e \"script prints n\" -e \"Site keyword rules\" plan.md", "description": "Confirm plan edits applied"}
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "python3 -m py_compile analysis.py && echo OK; rm -rf __pycache__\npython3 - <<'EOF'\np='plan.md'; s=open(p).read()\ns=s.replace(\"That is about 57 HTTP calls in total.
- plan14 h agoclaude-opus-5-5
Now the syntax check, plus small edits so plan.md matches the script.
- plan14 h agoclaude-opus-5-5
→ Write {"file_path": "/Users/igor/Work/me/bistro-research/state/runs/3/analysis.py", "content": "\"\"\"E:trial-burden-mismatch-1: cancer trials per unit cancer burden by World Bank income
- plan14 h agoclaude-opus-5-5
Both APIs match the plan. GLOBOCAN's cancer-list endpoint returns 404, so the script will print the world total for each site code as a check on the codes. Now writing analysis.py.
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /Users/igor/Work/me/bistro-research/state/runs/3; python3 -c \"\nimport httpx,json,time\nr=httpx.get('https://clinicaltrials.gov/api/v2/studies',params={'query.cond
- plan14 h agoclaude-opus-5-5
GLOBOCAN matches the plan. Next I'll check the CT.gov response shape and the GLOBOCAN cancer codes.
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /Users/igor/Work/me/bistro-research/state/runs/3; python3 -c \"\nimport httpx,json\nr=httpx.get('https://gco-api.iarc.fr/api/globocan/v3/2022/meta/populations/all/'
- plan14 h agoclaude-opus-5-5
A plan from an earlier attempt exists. Before I reuse it, I'll check the API shapes it depends on.
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cat plan.md; echo ----; head -c 4000 run_state.json; git log --oneline | head", "description": "Read existing plan and run state"}
- plan14 h agoclaude-opus-5-5
A plan.md already exists. Let me check it and run_state.json.
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "ls -la; cat ground.md", "description": "List directory and read ground file"}
- plan14 h agoclaude-opus-5-5
→ Write {"file_path": "/Users/igor/Work/me/bistro-research/state/runs/3/plan.md", "content": "# Plan: E:trial-burden-mismatch-1. Trials per unit cancer burden by country income\n\n**Pre-re
- plan14 h agoclaude-opus-5-5
Endpoints confirmed (GLOBOCAN 2022 API, ClinicalTrials.gov v2, World Bank). CT.gov returns 429 above about 1 request/s, so the script paces itself. Now writing plan.md.
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp/gco && python3 - <<'EOF'\nimport httpx,json\nc=httpx.Client(timeout=60)\nwb=c.get(\"https://api.worldbank.org/v2/country?format=json&per_page=400\").json()[1]\
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp/gco && python3 - <<'EOF'\nimport httpx,time,json,collections\nc=httpx.Client(timeout=60)\nB=\"https://clinicaltrials.gov/api/v2/studies\"\nwin=\"AREA[StudyType
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp/gco && python3 - <<'EOF'\nimport httpx,time,json,collections\nc=httpx.Client(timeout=60)\nB=\"https://clinicaltrials.gov/api/v2/studies\"\nwin=\"AREA[StudyType
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp/gco && python3 - <<'EOF'\nimport httpx,time,json\nc=httpx.Client(timeout=60)\nB=\"https://clinicaltrials.gov/api/v2/studies\"\nwin=\"AREA[StudyType]INTERVENTIO
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp/gco && python3 - <<'EOF'\nimport httpx,time,json\nc=httpx.Client(timeout=60)\nB=\"https://clinicaltrials.gov/api/v2/studies\"\nwin=\"AREA[StudyType]INTERVENTIO
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp/gco && python3 - <<'EOF'\nimport httpx,json,time,collections\nc=httpx.Client(timeout=60)\npops=c.get(\"https://gco-api.iarc.fr/api/globocan/v3/2022/meta/popula
- plan14 h agoclaude-opus-5-5
Still checking that the GLOBOCAN 2022 API works (it does), so analysis.py can pull burden data with a script.
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp/gco && python3 - <<'EOF'\nimport httpx,json,time\nc=httpx.Client(timeout=60)\ncan=c.get(\"https://gco-api.iarc.fr/api/globocan/v3/2022/meta/cancers/all/\").jso
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp/gco && python3 - <<'EOF'\nimport re\nt=open('main.js').read()\ns=set()\nfor m in re.finditer(r'data/(?:rate|population|numbers|cancer|age)',t):\n s.add(t[ma
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp/gco && python3 - <<'EOF'\nimport re\nt=open('main.js').read()\nfor m in sorted(set(re.findall(r'`\\$\\{[a-zA-Z_.]+\\}/?(?:data|meta)/[^`]{0,160}`',t)))[:60]: p
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp/gco && python3 - <<'EOF'\nimport httpx,re\nc=httpx.Client(timeout=30, follow_redirects=True)\nt=c.get(\"https://gco.iarc.who.int/today/assets/main-XI_1EMpI.js\
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "mkdir -p /tmp/gco && cd /tmp/gco && python3 - <<'EOF'\nimport httpx,re\nc=httpx.Client(timeout=30, follow_redirects=True)\nfor page in [\"https://gco.iarc.who.int/toda
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "cd /tmp && python3 - <<'EOF'\nimport httpx, json\nc=httpx.Client(timeout=30, follow_redirects=True)\ndef p(u, n=600):\n try:\n r=c.get(u); print(r.status_cod
- plan14 h agoclaude-opus-5-5
Let me probe candidate data endpoints (feasibility only, not running analysis.py).
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "head -c 3000 run_state.json; echo; python3 -c \"import httpx;print(httpx.__version__)\""}
- plan14 h agoclaude-opus-5-5
→ Bash {"command": "ls -la && cat ground.md"}
- ground14 h agoclaude-sonnet-5-5
`ground.md` is written. The literature supports the direction of the gradient, but I found no study that reports it as a trials-per-burden ratio by income group. The pre-registered >50-fold figure therefore has no direct anchor in what I retrieved. - **Direction:** LMICs carry most of the burden, but trials sit mostly in high-income countries. Two examples: 75.1% of bladder-cancer trials were high-income-only, and 81.2% of paediatrics-only trials had high-income sponsors. - **Magnitude:** the studies I read report shares or counts, not trials per unit burden. A share-based figure can sit well above or below 50-fold depending on the denominator. - **Casolino 2025 (the WHO trial landscape):** this is the most likely source of income-stratified counts, but its abstract came back empty. - **Gemini:** I didn't use `gemini_read`. I read the abstracts directly, so every claim rests on abstracts only. - **Searches:** nine queries on europepmc and openalex. I didn't run arxiv or a dedicated search for how burden estimates are made. The file has 15 references (all reached through search results) and lists 10 caveats. The main ones are the choice of burden denominator, unstable ratios when low-income countries have zero trials, and ambiguity in what a registry's country field means. It also names the public sources that can answer the question: GLOBOCAN, the ClinicalTrials.gov API, WHO ICTRP and the World Bank. It ends with a suggested analysis design. I haven't run any analysis or computed any ratio. The file is at `/Users/igor/Work/me/bistro-research/state/runs/3/ground.md`.
- ground14 h agoclaude-sonnet-5-5
→ Write {"file_path": "/Users/igor/Work/me/bistro-research/state/runs/3/ground.md", "content": "# Ground: trials per unit cancer burden by country income (E:trial-burden-mismatch-1)\n\n**P
- ground14 h agoclaude-sonnet-5-5
→ literature_search {"source": "openalex", "query": "globalization of industry-sponsored oncology trials low- and middle-income countries ClinicalTrials.gov analysis", "n": 8}
- ground14 h agoclaude-sonnet-5-5
→ literature_search {"source": "openalex", "query": "oncology clinical trials registered countries correlation with cancer incidence mortality GDP per capita", "n": 8}
- ground14 h agoclaude-sonnet-5-5
→ literature_search {"source": "openalex", "query": "Global Cancer Statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries", "n": 3}
- ground14 h agoclaude-sonnet-5-5
→ literature_search {"source": "europepmc", "query": "\"Global Cancer Observatory\" trials \"disease burden\" \"high-income\" registered oncology trials location", "n": 8}
- ground14 h agoclaude-sonnet-5-5
→ literature_search {"source": "openalex", "query": "clinical trials per cancer death country income registry ClinicalTrials.gov ICTRP", "n": 8}
- ground14 h agoclaude-sonnet-5-5
→ literature_search {"source": "openalex", "query": "randomized controlled trials cancer low- and middle-income countries proportion of global trials Fundytus", "n": 8}
- ground14 h agoclaude-sonnet-5-5
→ literature_search {"source": "openalex", "query": "The WHO global landscape of cancer clinical trials", "n": 5}
- ground14 h agoclaude-sonnet-5-5
→ literature_search {"source": "openalex", "query": "geographic inequality cancer clinical trials burden GLOBOCAN", "n": 10}
- ground14 h agoclaude-sonnet-5-5
→ literature_search {"source": "europepmc", "query": "ClinicalTrials.gov oncology trials low- and middle-income countries disease burden mismatch", "n": 10}
- ground14 h agoclaude-sonnet-5-5
→ literature_search {"source": "europepmc", "query": "global distribution of cancer clinical trials versus cancer burden by country income", "n": 10}
- ground14 h agoclaude-sonnet-5-5
→ ToolSearch {"query": "select:mcp__bistro__literature_search,mcp__bistro__gemini_read", "max_results": 5}