Bibliographic record
Abstract
Transparently documenting who and why a person is an author of a clinical trial report, as with any other article, is important since it allows research team members appropriate recognition and also likely helps reduce or avoid problems such as ghost authorship. Marušić and colleagues have previously proposed a five-step framework for attributing authorship of pharmaceutical company-sponsored clinical trials. The process is short, easily implemented, and can be used in addition to the authorship guidance provided by the International Committee of Medical Journal Editors. For the framework to gain optimal traction it is important that a strong implementation plan is developed and carried out across a broad spectrum of stakeholders. Authorship brings with it important responsibilities; authors must ensure that articles baring their names must be fit for purpose. This will help guarantee an increased value for published reports of clinical trials.
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 | 0.165 | 0.398 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.026 | 0.025 |
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".