Suggestions for improving guideline utility and trustworthiness
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
Clinical practice guideline (CPG) panels are expected to abide by standards that ensure their processes are multidisciplinary, systematic and unbiased.1 Unfortunately, many CPGs fail to satisfy these standards. Only a third of 130 US guidelines produced by subspecialty societies between 2006 and 2011,2 satisfied more than 50% of standards set by the Institute of Medicine (IOM—see table 1),1 relating to panel composition, conflicts of interest, evidence synthesis, reconciliation of different interpretations of evidence and enumeration of treatment harms. Guidelines from other countries demonstrate similar deficiencies.3 Editorialists have identified the need for transparent measures of guideline trustworthiness,4 and some professional societies have issued rigorous standards for their guideline development panels.5 The fact that comparative studies have identified guidelines that more consistently meet most IOM standards6 ,7 suggests that it is possible for more guideline panels to improve the quality and rigour of their processes. View this table: Table 1 Institute of Medicine standards for developing trustworthy clinical practice guidelines1 In an era when clinicians are increasingly using CPGs to inform their care and guidelines are being increasingly used as reference standards for clinical audits, pay for performance schemes, public scorecards and medical litigation, guidelines must be both rigorously developed and mindful of challenges in implementing their recommendations. In this article, we explore problematic issues that have received limited attention to date in guideline appraisal tools and commentaries. A medical defence organisation in Australia recently warned doctors that conflicting guideline recommendations around prostate cancer screening using prostatic-specific antigen (PSA) testing may render them individually liable to claims of delayed diagnosis.8 In this case, CPG issued from the Royal Australasian College of General Practitioners9 stated that men aged 55–69 years should not be offered PSA testing routinely whereas CPG from the Urological Society of Australia and New …
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: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
| gpt | Metaresearch Domain: Evaluation · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.003 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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