Improving the Transparency and Replicability of Consensus Methods: Respiratory Medicine as a Case Example
Bibliographic record
Abstract
Acknowledgments The authors would like to thank Paul Blazey (Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada), William T Gattrell (Bristol Myers Squibb, Uxbridge, UK), Keith Goldman (Global Medical Affairs, AbbVie, North Chicago, Illinois, USA), Niall Harrison (OPEN Health Communications, Marlow, UK), Ellen L Hughes (Camino Communications Ltd, UK), Sir Amritpal Hungin (Faculty of Medical Sciences, Newcastle University, Newcastle, UK), Patricia Logullo (Centre for Statistics in Medicine, University of Oxford, and EQUATOR Network UK Centre, Oxford, United Kingdom), Amy Price (Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Dartmouth, NH, USA), David Tovey (Journal of Clinical Epidemiology, London, UK), and Esther J van Zuuren (Leiden University Medical Centre, Leiden, Netherlands) for their contributions to the development of the ACCORD reporting guideline.
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 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 | 0.072 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".