Fate of Registered Studies From London, Ontario
Bibliographic record
Abstract
Introduction: Lack of study publication leads to bias in the scientific literature. It is important to better understand this phenomenon and find methods for mitigation.\nResearch Question: How many clinical trials registered on ClinicalTrials.gov in London, Ontario are started, completed, and published?\nMethods: Data from all studies in the ClinicalTrials.gov registry associated with London, Ontario were collected, from registry conception until the end of 2017. We determined whether these registered studies were published by July 2020 and whether their first publication included their planned primary outcome at all. Main factors associated with non-publication were assessed using multivariable log-binomial regression. Multivariable modified Poisson regression was used to assess the association between enrollment size and publication. Time to publication was assessed using multiple linear regression.\nResults: Of the registered studies (n = 2446), only 38% were published and 30% with their planned primary outcome. Median time to publication post-start was 53 months [IQR: 36, 75]. Factors associated with publication were randomized design, prospective registration, industry funding, drug study, and enrollment size (p < 0.05). Factors associated with shorter time to publication were positive results, prospective registration, and industry funding, while drug studies were associated with longer time to publication (p < 0.05). Surgical studies seemed to have decreased chances of publication and lengthened time to publication but was not statistically significant in either case.\nConclusions: A substantial proportion of clinical trials from London, Ontario remained unpublished. The factors predictive of non-publication and time to publication suggest potential avenues for increasing publication rates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".