Bibliographic record
Abstract
One of the key elements in evidence-based medicine (EBM) is reliable information from research on the benefits and harms of specific interventions, actions or strategies. This is true for resolving uncertainties about interventions that might be used to treat illnesses or improve well-being and also for choosing screening or diagnostic tests, understanding risk factors and estimating the current and future burden of disease. As the principles and practice of EBM have become more accepted and widespread over the last few decades, there has been an accompanying tremendous growth in the number of systematic reviews and wider recognition of their value. From sporadic examples before the 1980s, through the estimated 3000 that were indexed in MEDLINE during the two decades to 2000,1 200 000 or more might now be available.2 More than 10 000 systematic reviews are published every year, and over 30 000 are registered in the prospective registry, PROSPERO.3 They are a vital part of EBM, and many of the reasons that we value them today have echoes in history. We have written elsewhere about this history …
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: Methods · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Editorial 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 | 0.237 | 0.585 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.027 | 0.042 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.036 | 0.083 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".