Certainty of evidence and intervention's benefits and harms are key determinants of guidelines’ recommendations
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
OBJECTIVE: Many factors are postulated to affect guidelines developments. We set out to identify the key determinants. STUDY DESIGN AND SETTING: a) Web-based survey of 12 panels of 153 "voting" members who issued 2941 recommendations; b) qualitative analysis of 13 panels of 311 attendees (panel members, systematic review teams and observers). RESULTS: Compared with "no recommendations", when intervention's benefit outweigh harms (BH-balance), probability of issuing strong recommendations in favor of intervention was 0.22 (95%CI: 0.08 to 0.36) when certainty of evidence (CoE) was very low; 0.5 (95%CI:0.36 to 0.63) when low; 0.74 (95%CI 0.61 to 0.87) when moderate and 0.85 (95%CI:0.71 to 1.00) when high. No other postulated factor significantly affected recommendations. The findings are consistent with a J- curve model when recommendations are issued in favor but not against an intervention. Panelists often changed their judgments as a result of the meeting discussion (67% for CoE to 92% for balance between benefits and harms). The panels spent over 50% of their time debating CoE; the chairs and co-chairs dominated discussion. CONCLUSIONS: CoE and BH-balance are key determinants of recommendations in favor of an intervention. Chairs and co-chairs dominate discussion. Panelists often change their judgments as a result of panel deliberation.
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.
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 | 0.029 | 0.573 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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