Bibliographic record
Abstract
Transparency in clinical trials is an issue under considerable scrutiny at present, and rightfully so, given that people's lives are both used as a resource in such research and affected by its results. Hence, the results of clinical trials conducted should be made available in both journal articles and in open access trial registries (like ClinicalTrials.gov). The latter not only make research results more accessible to the general public but are also considered essential resources in systematic reviews to avoid publication bias. Yet, up to 89% of clinical trials conducted at McGill University are not reported in clinical trial registries, and up to 37% of the trials are not published. However, since most McGill University researchers use public funding to conduct trials on human subjects, they have an obligation to make their research freely accessible. Spreading awareness regarding this issue among key stakeholders is a possible way to reduce this problem and increase the transparency of clinical research at McGill University.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.147 | 0.481 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.223 | 0.035 |
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".