Finding Current Best Evidence in Endocrinology
Bibliographic record
Abstract
Two years ago we presented various resources likely to provide the best research evidence concerning endocrine disorders ( 1 ). At the time, an already overwhelming array of resources existed and have since grown. Clinicians are bombarded by information arriving by regular mail and e-mail, in educational rounds and seminars, and through countless other avenues. Many resources make claims to be “evidence-based” or “the only resource you need,” and quite often they are free. With such an onslaught of information, how can you pay attention to any of them, let alone summon the time and energy to sort through all of them to find resources truly useful to your own clinical practice? Indeed, a recent study of primary care literature indicated that 627.5 h of physician effort would be required to evaluate the 7287 articles published per month in five primary care journal review services ( 2 ). The plethora of evidence-based resources now available means that more than ever, clinicians must be discriminating about how to make best use of them. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".