Proposed triggers for retiring a living systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Living systematic reviews (LSRs) are systematic reviews that are continually updated, incorporating relevant new evidence as it becomes available. LSRs are critical for decision-making in topics where the evidence continues to evolve. It is not feasible to continue to update LSRs indefinitely; however, guidance on when to retire LSRs from the living mode is not clear. We propose triggers for making such a decision. The first trigger is to retire LSRs when the evidence becomes conclusive for the outcomes that are required for decision-making. Conclusiveness of evidence is best determined based on the GRADE certainty of evidence construct, which is more comprehensive than solely relying on statistical considerations. The second trigger to retire LSRs is when the question becomes less pertinent for decision-making as determined by relevant stakeholders, including people affected by the problem, healthcare professionals, policymakers and researchers. LSRs can also be retired from a living mode when new studies are not anticipated to be published on the topic and when resources become unavailable to continue updating. We describe examples of retired LSRs and apply the proposed approach using one LSR about adjuvant tyrosine kinase inhibitors in high-risk renal cell carcinoma that we retired from a living mode and published its last update.
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.
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: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.876 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it