Bibliographic record
Abstract
Clinical practice guidelines have improved in quality over the past 10 years by adhering to a few basic principles, such as conducting thorough systematic reviews of relevant evidence and grading the recommendations and the quality of the underlying evidence. The large number of systems of measuring the quality of evidence and recommendations that have emerged are, however, confusing.1 The mission of the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) working group is to help resolve the confusion among the different systems of rating evidence and recommendations. The group has wide representation from many organisations including the Agency for Healthcare Research and Quality in the US, the National Institute for Clinical Excellence for England and Wales, and the World Health Organization. Developing a new uniform rating system is challenging because all systems have limitations and because many organisations have invested a great deal of time and effort to develop their rating systems and are understandably reluctant to adopt a new system. The GRADE working group first published the results of its work in 2004 in the BMJ.2 A simpler, clinically oriented description will soon be published.3 GRADE has taken care to ensure its suggested system is simple to use and applicable to a wide variety of clinical recommendations that span the full spectrum of medical specialties and clinical care. The GRADE system classifies recommendations in 1 of 2 levels—strong and weak—and quality of evidence into 1 of 4 levels—high, moderate, low, and very low. Evidence based on randomised controlled trials (RCTs) begins with a top rating on GRADE’s 4 level quality of evidence classification (table …
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 | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | 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 | 0.300 | 0.604 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.029 | 0.019 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.022 | 0.017 |
| Research integrity | 0.028 | 0.042 |
| Insufficient payload (model declined to judge) | 0.013 | 0.015 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".