The Reporting of Observational Clinical Functional Magnetic Resonance Imaging Studies: A Systematic Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.285 | 0.661 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".