Rating the certainty in evidence in the absence of a single estimate of effect
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
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.
- Candidate categories
- Metaresearch
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: ObservationalConsensus signal: Observational
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.134
- Threshold uncertainty score
- 0.994
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.050 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
When studies measure or report outcomes differently, it may not be feasible to pool data across studies to generate a single effect estimate (ie, perform meta-analysis). Instead, only a narrative summary of the effect across different studies might be available. Regardless of whether a single pooled effect estimate is generated or whether data are summarised narratively, decision makers need to know the certainty in the evidence in order to make informed decisions. In this guide, we illustrate how to apply the constructs of the GRADE (Grading of Recommendation, Assessment, Development and Evaluation) approach to assess the certainty in evidence when a meta-analysis has not been performed and data were summarised narratively.
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.
The record
- Venue
- Evidence-Based Medicine
- Topic
- Health Policy Implementation Science
- Field
- Health Professions
- Canadian institutions
- McMaster UniversityImpact
- Funders
- not available
- Keywords
- CertaintyGrading (engineering)Meta-analysisComputer scienceEconometricsPsychologyActuarial scienceMathematicsEconomicsMedicineEngineering
- Has abstract in OpenAlex
- yes