The Pandora’s Box of Evidence Synthesis and the case for a living Evidence Synthesis Taxonomy
Bibliographic record
Abstract
Have we, as an evidence-based health community, opened the Pandora's box of evidence synthesis? There now exists a plethora of overlapping evidence synthesis approaches and duplicate, redundant and poor-quality reviews.1-4 After years of advocating for the need for systematic reviews of the evidence, there is a risk that this message been disseminated too widely and has been misinterpreted in this process. We have reached a point where in some fields more reviews exist than clinical trials, where same topic reviews are being conducted in parallel, and evidence syntheses possess limited utility for decision-making because of their poor quality or poor reporting.To paraphrase the late Douglas Altman,5 it is possible we are now at a stage where we need less reviews, better reviews and reviews done for the right reason - as opposed to the current state of mass production (approximately 80 reviews per day)6.
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: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.619 | 0.697 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.008 |
| Bibliometrics | 0.029 | 0.020 |
| Science and technology studies | 0.011 | 0.091 |
| Scholarly communication | 0.055 | 0.080 |
| Open science | 0.014 | 0.027 |
| Research integrity | 0.044 | 0.085 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".