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Record W3150860698 · doi:10.1136/bmjebm-2020-111652

Completeness of reporting for systematic reviews of point-of-care ultrasound: a meta-research study

2021· article· en· W3150860698 on OpenAlexaff
Ross Prager, Michael Pratte, Andrew Guy, Sudarshan Bala, Roudi Bachar, Daniel Kim, Scott J. Millington, Jean‐Paul Salameh, Trevor A. McGrath, Matthew D. F. McInnes

Bibliographic record

VenueBMJ evidence-based medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsVancouver General HospitalMcMaster UniversityQueen's UniversityUniversity of British ColumbiaUniversity of Ottawa
Fundersnot available
KeywordsChecklistSystematic reviewMedicineCochrane LibraryMEDLINEMeta-analysisSubgroup analysisPublication biasMedical physicsPathologyPsychology

Abstract

fetched live from OpenAlex

OBJECTIVES: Systematic reviews are often considered among the highest quality of evidence. Completely reported systematic reviews, however, are required so readers can assess for generalisability of the research to practice and risk of bias. The objective of this study was to assess the completeness of reporting for systematic reviews assessing the diagnostic accuracy of point-of-care ultrasound (POCUS) using the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Diagnostic Test Accuracy (PRISMA-DTA) checklist that was published in 2018. DESIGN AND SETTING: databases were searched, with no date restriction, on March 1st, 2020 for systematic reviews assessing the diagnostic accuracy of POCUS. Adherence to PRISMA-DTA for the main text and abstract was scored independently and in duplicate using a modified checklist. Prespecified subgroup analyses were performed. MAIN OUTCOME MEASURES: The primary outcome was the mean PRISMA-DTA checklist adherence for the full-text and abstract. RESULTS: A total of 71 studies published from 2008 to 2020 met the inclusion criteria. The overall adherence for the full-text was moderate: 19.8 out of 26.0 items (76%) and for the abstract was 7.0 out of 11.0 items (64%). Although many items in the PRISMA-DTA checklist were frequently reported, several were r infrequently reported (<33% of studies), including item 5 (protocol registration), item D2 (minimally acceptable test accuracy) and item 14 (variability in target condition, index test and reference standards). Subgroup analyses showed a higher PRISMA-DTA mean adherence (SD) for high impact journals (20.9 (2.52) vs 18.9 (1.95); p<0.001), studies including supplemental materials (20.6 (2.48) vs 18.9 (2.28); p=0.004), studies citing adherence to PRISMA reporting guidelines (20.4 (1.95) vs 19.0 (3.00); p=0.038) and studies published in journals endorsing PRISMA guidelines (20.2 (2.47) vs 18.6 (2.37); p=0.025). There was variable adherence based on journal of publication (p=0.006), but not for study population (adult vs paediatric vs mixed) (p=0.62), year of publication (p=0.94), body region (p=0.78) or country (p=0.40). There was no variability in abstract adherence based on whether the abstract was structured with subheadings or not (p=0.25). A Spearman's correlation found moderate correlation between higher word counts and abstractand full-text adherence (R=0.45, p<0.001 and R=0.38, p=0.001), respectively. CONCLUSIONS: Overall, the reporting of POCUS diagnostic accuracy systematic reviews and meta-analyses was moderate. We identified deficits in several key areas including the preregistration of systematic reviews in an online repository, handling of multiple definitions of target conditions, index tests and reference standards and specifying minimally acceptable test accuracy. Prospective registration of reviews and detailed reporting as per PRISMA-DTA during the research process could improve reporting completeness. At an editorial level, word count and supplemental material limitations may impede reporting completeness, whereas endorsement of reporting guidelines on journal websites could improve reporting.

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)0.026
metaresearch head score (Gemma)0.351
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.351
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.650
GPT teacher head0.577
Teacher spread0.073 · 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 designSystematic review
Domainnot available
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

Citations5
Published2021
Admission routes1
Has abstractyes

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