Standards for Reporting of Diagnostic Accuracy involving Intraoperative Neurophysiological Monitoring
Bibliographic record
Abstract
Title Standards for Reporting of Diagnostic Accuracy using Intraoperative Neurophysiological Monitoring (STARD-IONM) Objective Intraoperative neurophysiological monitoring (IONM) plays a critical role in preserving functional integrity during surgery, yet it is challenging to compare studies due to methodological heterogeneity and inconsistent reporting. We developed the STARD-IONM extension to improve the transparency, completeness, and comparability of IONM studies. Methods The STARD-IONM initiative followed a three-phase consensus. Phase 1 convened a IONM expert panel to discuss and define the rationale and scope. Phase 2 involved structured item-level review of existing STARD items in the context of IONM, applied to published studies with iterative feedback. Phase 3 will include broader community engagement via preprints, outreach to professional societies, and public commentary. Results A systematically selected review of IONM studies revealed the underreporting of at key methodological items such as handling missing data (7%), adverse events (11%), and blinding of test and outcomes (22%). A STARD-IONM checklist with recommendations for reporting IONM studies with IONM specific examples were developed. Community feedback emphasized challenges unique to IONM, including the classification of reversible IONM changes, and variability in reference standards. Conclusions The STARD-IONM framework addresses critical gaps in the reporting of diagnostic accuracy studies involving IONM. It represents an application of the STARD criteria, wherein the original checklist has been adapted and supplemented with guidelines for IONM studies. Significance Standardized reporting will facilitate enhanced adherence to methodological standards, increase reproducibility and strengthen the evidence base for the safe and effective use of IONM, which is expected to improve clinical decision-making.
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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 | 0.597 | 0.824 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.029 | 0.015 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.012 | 0.023 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".