The Prevalence of Incidental Findings in Multiple Sclerosis Patients
Bibliographic record
Abstract
BACKGROUND: Incidental findings arising from imaging research have important implications for patient safety. Magnetic resonance imaging is widespread in multiple sclerosis (MS) studies and care, yet the prevalence rate of incidental findings in MS is poorly defined. The absence of such reports in the MS literature suggests that such findings may be deemed inappropriate for documentation in research publications, or possibly, not fully reported at all. OBJECTIVE: We sought to document incidental findings from a study designed to detect features of chronic cerebrospinal venous insufficiency (CCSVI) in MS patients and control subjects. METHODS: Magnetic resonance images were obtained as part of a prospective study conducted between October 2010 and September 2012. Patients with MS (relapsing-remitting, primary progressive, secondary progressive), clinically isolated syndromes, and neuromyelitis optica and age/sex-matched healthy controls were included. All images were reviewed by neuro-radiologists for quality-control purposes. RESULTS: Magnetic resonance imaging was successfully obtained in 166 participants (110 patients, 56 controls). Incidental abnormalities (n = 33) were detected in 15% of patients (n = 17) and 27% of controls (n = 15), comprising 19% overall (n = 32). CONCLUSIONS: The prevalence of incidental findings from the MS population was not significantly different from the control population. However, the overall prevalence was high and warrants a careful management strategy for future imaging studies. Prévalence des découvertes fortuites chez les patients atteints de sclérose en plaques.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".