What does expert opinion in guidelines mean? a meta-epidemiological study
Bibliographic record
Abstract
Guidelines often use the term expert opinion (EO) to qualify recommendations. We sought to identify the rationale and evidence type in EO recommendations. We searched multiple databases and websites for contemporary guidelines published in the last decade that used the term EO. We identified 1106 references, of which 69 guidelines were included (2390 recommendations, of which 907 were qualified as EO). A rationale for using EO designation was not provided in most (91%) recommendations. The most commonly cited evidence type was extrapolated from studies that did not answer guideline question (40% from randomised trials, 38% from observational studies and 2% from case reports or series). Evidence extrapolated from populations that were different from those addressed in the guideline was found in 2.5% of EO recommendations. We judged 5.6% of EO recommendations as ones that could have been potentially labelled as good practice statements. None of the EO recommendations were explicitly described as being solely dependent on the clinical experience of the panel. The use of EO as a level of evidence in guidelines remains common. A rationale for such use is not explicitly provided in most instances. Most of the time, evidence labelled as EO was indirect evidence and occasionally was very low-quality evidence derived from case series. We posit that the explicit description of evidence type, as opposed to using the label EO, may add clarity and transparency and may ultimately improve uptake of recommendations.
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 | MetaresearchMeta-epidemiology (broad)Meta-epidemiology (narrow) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.087 | 0.365 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".