Eligibility criteria for HIV clinical trials and generalizability of results: the gap between published reports and study protocols
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Applicability of randomized controlled clinical trial (RCT) results to 'real world' situations is dependent on the comparability of trial participants to general patient populations. A full disclosure of criteria employed for trial enrollment is necessary for clinicians to assess generalizability. We sought to assess both the impact on generalizability and the disclosure rate of enrollment criteria for 32 major HIV RCTs in the AIDS Clinical Trial Group (ACTG) and Community Programs for Clinical Research on AIDS (CPCRA) trial networks. DESIGN AND METHODS: Eligibility criteria were compared in complete protocols to criteria listed in publications from these 32 NIH-funded HIV RCTs. We then applied these criteria to the Women's Interagency HIV Study (WIHS), the largest cohort study of HIV-infected women in the US. RESULTS: When applied to WIHS, eligibility criteria from protocols excluded 0-67.6% (median 42%) of WIHS participants (50.6% excluded from ACTG trials). Eligibility criteria in publications excluded 0-62% (median 19.6%) of WIHS (21.2% excluded from ACTG trials). The number of women in WIHS seemingly ineligible for trial participation per enrollment criteria listed in publications averaged only 60% of those actually excluded based on the protocols. CONCLUSIONS: We found that HIV RCT eligibility criteria excluded a large proportion of a representative cohort of HIV-infected women from trial participation. Furthermore, trial publications are not fully reflective of protocols in terms of disclosing eligibility criteria. Standardization and full disclosure of trial methodology will allow clinicians and researchers to more fully assess the generalizability of findings to their patient populations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.202 | 0.445 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it