Additional file 2 of Efficacy of interventions targeted at physician prescribers of opioids for chronic non-cancer pain: an overview of systematic reviews
Bibliographic record
Abstract
Additional file 2: Supplementary. Table S1. Search Strategy to Identify Eligible Systematic Reviews (MEDLINE via Ovid). Table S2. Search Strategy to Identify Eligible Systematic Reviews (Embase via Ovid). Table S3. Search Strategy to Identify Eligible Systematic Reviews (PsycINFO via Ovid). Table S4. Search Strategy to Identify Eligible Systematic Reviews (Cochrane Database of Systematic Reviews). Table S5. Search Strategy to Identify Eligible Systematic Reviews (Epistemonikos). Table S6. Citation Matrix. Table S7. Risk of Bias of Primary Studies for Mathieson et al. using the Cochrane V1 Risk of Bias Assessment. Table S8. Risk of Bias of Primary Studies for Puac-Polanco et al. using the Ottawa Newcastle Assessment Method. Table S9. Risk of Bias of Primary Studies for Picco et al. using the Mixed Methods Appraisal Tool
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 | 0.006 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.841 | 0.047 |
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".