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Record W2625969387 · doi:10.1111/acem.13233

Adherence to Standards for Reporting Diagnostic Accuracy in Emergency Medicine Research

2017· review· en· W2625969387 on OpenAlexaff
Lucas Gallo, Nadia Hua, Mathew Mercuri, Angela Silveira, Andrew Worster

Bibliographic record

VenueAcademic Emergency Medicine · 2017
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of OttawaMcMaster University
Fundersnot available
KeywordsMedicineDiagnostic accuracyMEDLINEEmergency departmentTest (biology)Medical physicsDiagnostic testEmergency medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Diagnostic tests are used frequently in the emergency department (ED) to guide clinical decision making and, hence, influence clinical outcomes. The Standards for Reporting of Diagnostic Accuracy (STARD) criteria were developed to ensure that diagnostic test studies are performed and reported to best inform clinical decision making in the ED. OBJECTIVE: The objective was to determine the extent to which diagnostic studies published in emergency medicine journals adhered to STARD 2003 criteria. METHODS: Diagnostic studies published in eight MEDLINE-listed, peer-reviewed, emergency medicine journals over a 5-year period were reviewed for compliance to STARD criteria. RESULTS: A total of 12,649 articles were screened and 114 studies were included in our study. Twenty percent of these were randomly selected for assessment using STARD 2003 criteria. Adherence to STARD 2003 reporting standards for each criteria ranged from 8.7% adherence (criteria-reporting adverse events from performing index test or reference standard) to 100% (multiple criteria). CONCLUSION: Just over half of STARD criteria are reported in more than 80% studies. As poorly reported studies may negatively impact their clinical usefulness, it is essential that studies of diagnostic test accuracy be performed and reported adequately. Future studies should assess whether studies have improved compliance with the STARD 2015 criteria amendment.

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)0.612
metaresearch head score (Gemma)0.961
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.6120.961
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0250.004
Bibliometrics0.0040.008
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0120.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0800.002

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 head0.962
GPT teacher head0.755
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2017
Admission routes1
Has abstractyes

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