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Record W2763039707 · doi:10.1186/s13012-017-0651-3

Updated clinical guidelines experience major reporting limitations

2017· article· en· W2763039707 on OpenAlexaff
Robin W.M. Vernooij, Laura Martínez García, Iván D. Flórez, Laura Hildago Armas, Michiel H.F. Poorthuis, Melissa Brouwers, Pablo Alonso‐Coello

Bibliographic record

VenueImplementation Science · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcMaster UniversityImpact
FundersInstituto de Salud Carlos IIIEuropean Regional Development FundUniversitat Autònoma de Barcelona
KeywordsMedicineHealth informaticsHealth services researchHealth administrationPublic healthFamily medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The Checklist for the Reporting of Updated Guidelines (CheckUp) was recently developed. However, so far, no systematic assessment of the reporting of updated clinical guidelines (CGs) exists. We aimed to examine (1) the completeness of reporting the updating process in CGs and (2) the inter-observer reliability of CheckUp. METHODS: We conducted a systematic assessment of the reporting of the updating process in a sample of updated CGs using CheckUp. We performed a systematic search to identify updated CGs published in 2015, developed by a professional society, reporting a systematic review of the evidence, and containing at least one recommendation. Three reviewers independently assessed the CGs with CheckUp (16 items). We calculated the median score per item, per domain, and overall, converting scores to a 10-point scale. Multiple linear regression analyses were used to identify differences according to country, type of organisation, scope, and health topic of updated CGs. We calculated the intraclass coefficient (ICC) and 95% confidence interval (95% CI) for domains and overall score. RESULTS: We included in total 60 updated CGs. The median domain score on a 10-point scale for presentation was 5.8 (range 1.7 to 10), for editorial independence 8.3 (range 3.3 to 10), and for methodology 5.7 (range 0 to 10). The median overall score on a 10-point scale was 6.3 (range 3.1 to 10). Presentation and justification items at recommendation level (respectively reported by 27 and 38% of the CGs) and the methods used for the external review and implementing changes in practice were particularly poorly reported (both reported by 38% of the CGs). CGs developed by a European or international institution obtained a statistically significant higher overall score compared to North American or Asian institutions (p = 0.014). Finally, the agreement among the reviewers on the overall score was excellent (ICC 0.88, 95% CI 0.75 to 0.95). CONCLUSIONS: The reporting of updated CGs varies considerably with significant room for improvement. We recommend using CheckUp to assess the updating process in updated CGs and as a blueprint to inform methods and reporting strategies in updating.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.634
metaresearch head score (Gemma)0.879
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.366
Threshold uncertainty score0.451

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6340.879
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0190.023
Science and technology studies0.0030.010
Scholarly communication0.0130.010
Open science0.0080.012
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0080.003

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 head0.851
GPT teacher head0.724
Teacher spread0.127 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainReporting
GenreEmpirical

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

Citations15
Published2017
Admission routes1
Has abstractyes

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