Peer Review of “All You Need Is Context: Clinician Evaluations of Various Iterations of a Large Language Model–Based First Aid Decision Support Tool in Ghana (Preprint)”
Bibliographic record
Abstract
All You Need Is Context: Clinician Evaluations of Various Iterations of a Large Language Model-Based First Aid Decision Support Tool in Ghana."This review is the result of a virtual collaborative live review discussion organized and hosted by PREreview and JMIR Publications on June 20, 2024.The discussion was joined by 15 people: 2 facilitators, 2 members of the JMIR Publications team, 2 authors, and 9 live review participants, including 3 who agreed to be named, Aswathi Surendran, Khushboo Thaker, Arya Rahgozar, and Emmanuel Adamolekun, but did not contribute to the final composition of this review.The authors of this review have dedicated additional asynchronous time over the course of 2 weeks to help compose this final report using the notes from the live review.We thank all participants who contributed to the discussion and made it possible for us to provide feedback on this preprint.
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 | 0.040 | 0.410 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.080 | 0.031 |
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".