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Record W2024080854 · doi:10.1371/journal.pone.0094412

The Reporting of Observational Clinical Functional Magnetic Resonance Imaging Studies: A Systematic Review

2014· review· en· W2024080854 on OpenAlexaff
Qing Guo, Melissa Parlar, Wanda Truong, Geoffrey B. Hall, Lehana Thabane, Margaret C. McKinnon, Ron Goeree, Eleanor Pullenayegum

Bibliographic record

VenuePLoS ONE · 2014
Typereview
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsPrograms for Assessment of Technology in Health Research InstituteBaycrest HospitalUniversity of CalgarySt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsObservational studyChecklistMEDLINESample size determinationFunctional magnetic resonance imagingMeta-analysisStrengthening the reporting of observational studies in epidemiologyPublication biasMedicineMagnetic resonance imagingConfoundingPsychologyConfidence intervalClinical psychologyMedical physicsStatisticsPathologyInternal medicineCognitive psychologyRadiologyMathematics

Abstract

fetched live from OpenAlex

INTRODUCTION: Complete reporting assists readers in confirming the methodological rigor and validity of findings and allows replication. The reporting quality of observational functional magnetic resonance imaging (fMRI) studies involving clinical participants is unclear. OBJECTIVES: We sought to determine the quality of reporting in observational fMRI studies involving clinical participants. METHODS: We searched OVID MEDLINE for fMRI studies in six leading journals between January 2010 and December 2011.Three independent reviewers abstracted data from articles using an 83-item checklist adapted from the guidelines proposed by Poldrack et al. (Neuroimage 2008; 40: 409-14). We calculated the percentage of articles reporting each item of the checklist and the percentage of reported items per article. RESULTS: A random sample of 100 eligible articles was included in the study. Thirty-one items were reported by fewer than 50% of the articles and 13 items were reported by fewer than 20% of the articles. The median percentage of reported items per article was 51% (ranging from 30% to 78%). Although most articles reported statistical methods for within-subject modeling (92%) and for between-subject group modeling (97%), none of the articles reported observed effect sizes for any negative finding (0%). Few articles reported justifications for fixed-effect inferences used for group modeling (3%) and temporal autocorrelations used to account for within-subject variances and correlations (18%). Other under-reported areas included whether and how the task design was optimized for efficiency (22%) and distributions of inter-trial intervals (23%). CONCLUSIONS: This study indicates that substantial improvement in the reporting of observational clinical fMRI studies is required. Poldrack et al.'s guidelines provide a means of improving overall reporting quality. Nonetheless, these guidelines are lengthy and may be at odds with strict word limits for publication; creation of a shortened-version of Poldrack's checklist that contains the most relevant items may be useful in this regard.

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)0.285
metaresearch head score (Gemma)0.661
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.715
Threshold uncertainty score0.881

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2850.661
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0100.007
Bibliometrics0.0190.024
Science and technology studies0.0020.006
Scholarly communication0.0080.012
Open science0.0060.005
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.548
GPT teacher head0.425
Teacher spread0.123 · 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 designSystematic review
DomainReporting
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

Citations32
Published2014
Admission routes1
Has abstractyes

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