Beoordelen van bewijs voor nieuwe behandelingen
Bibliographic record
Abstract
For new interventions, the results of sequential randomized or non-randomized trials and meta-analysis can differ significantly. Evaluation of the evidence for the effect of a new treatment is a complex interplay of several factors, including the methodological design, the risk of a coincidental finding and applicability in practice. For proper appraisal of the design of trials, the use of aggregate scores should be avoided and individual study limitations should be mentioned. With the use of additional analyses we can now test whether meta-analyses contain sufficient data to find potentially relevant differences. The new 'Grading of recommendations assessment, development and evaluation' (GRADE) system is a consensus guideline that combines multiple factors into an easily interpreted judgment of the strength of evidence. Concise presentation of important quality factors for each outcome significantly increases clarity. A more realistic assessment of the reliability of the evidence decreases the risk of major fluctuations in treatment policy
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 | 0.053 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.048 | 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, 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".