Modifications to the NEATS instrument for more appropriate and reproducible assessment of guidelines trustworthiness
Bibliographic record
Abstract
Abstract Rational The National Guideline Clearinghouse Extent of Adherence to Trustworthy Standards (NEATS) instrument measures the adherence of clinical practice guidelines to the trustworthiness standards proposed by the Institute of Medicine. However, rather than trustworthiness or methodological quality, the NEATS instrument evaluates the quality of reporting in addressing the management of conflict of interest of guideline development group members, systematic review process and assessment of the certainty of the evidence, rating the strength of recommendations, and updating plans. Furthermore, the NEATS instrument instructions on rating items are sufficiently limited that they may compromise the reproducibility of the instrument. Methods (Modifications to the NEATS Instrument) In the context of a specific methodological research project, we developed modifications to the NEATS instrument that include modifying the wording of items to capture trustworthiness rather than reporting quality and creating an algorithm for all items that maps responses to a series of prompting questions and guides the user in arriving at a rating from 1 to 5 or yes, no, or unknown, as applicable. Implications Modifications to the NEATS instrument present a structured and practical framework to assess the degree to which clinical practice guidelines are trustworthy, and they ensure that items evaluate trustworthiness and improve the reproducibility of assessments across a range of raters' level of expertise in guideline methodology.
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How this classification was reachedexpand
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 | 0.346 | 0.642 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".