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Record W4401038341 · doi:10.1002/gin2.12025

Modifications to the NEATS instrument for more appropriate and reproducible assessment of guidelines trustworthiness

2024· article· en· W4401038341 on OpenAlexaff
Maryam Ghadimi, Romina Brignardello‐Petersen

Bibliographic record

VenueClinical and Public Health Guidelines · 2024
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsTrustworthinessComputer scienceComputer security

Abstract

fetched live from OpenAlex

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.

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

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.698
GPT teacher head0.638
Teacher spread0.060 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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