Bibliographic record
Abstract
The Centre for Evidence-Based Medicine in Oxford offers workshops on practising and teaching evidence-based medicine (EBM). Having attended 2 of these and co-tutored in a third, I thought I would offer some advice to attendees as they return to their home bases. As anyone who has attended such workshops knows, it is wonderful to be surrounded by like-minded individuals who also wish to advance their knowledge and expertise in the various facets of EBM such as framing questions, searching the literature, critical appraisal, etc. The organisers and tutors are always enthusiastic, supportive, and helpful for the EBM neophytes. And who could argue with the venues at Oxford Colleges and English pubs? There is an appropriate mixture of plenary and small group sessions, and time, unfortunately, passes much too quickly The long trip home may be the first opportunity to reflect on what one is going to do with the newly learned knowledge or skills. It would be wise to jot down some notes before too much time passes, while the ideas are fresh and before one gets distracted by the daily tasks, particularly those put on hold during the workshop. For the workshop to have been truly successful, some change has to occur, and listing some of the possibilities is a suggested first step. Be prepared for the possibility of some letdown. Whether you are …
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.111 | 0.085 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".