Linked publications from a single trial: a thread of evidence
Bibliographic record
Abstract
Trials was launched with the ambition of providing authors with the opportunity to provide all the necessary detail for a true and complete scientific record [ 1 ]. It has long pushed for the communication of all outcome measures in health-related randomized controlled trials, as well as varying analyses and interpretations, and in-depth descriptions of what was done and what was learnt. An integral part of this was, of course, the publication of study protocols, which had rarely been possible in paper-based journals [ 2 ]. A published protocol establishes precedence, allows more detailed discussion of methodological issues and can be referenced when reporting the main trial results [ 3 ].
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 | 0.046 | 0.192 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.020 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".