Diffusion-Weighted Magnetic Resonance Imaging of Cholesteatoma Using PROPELLER at 1.5T: A Single-Centre Retrospective Study
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the sensitivity, specificity, and positive and negative predictive values of the diffusion-weighted periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique in the detection of cholesteatoma at our institution with surgical confirmation in all cases. METHODS: A retrospective review of 21 consecutive patients who underwent diffusion-weighted PROPELLER magnetic resonance imaging (MRI) on a 1.5T MRI scanner prior to primary or revision/second-look surgery for suspected cholesteatoma from 2009-2012 was performed. RESULTS: Diffusion-weighted PROPELLER had a sensitivity of 75%, specificity of 60%, positive predictive value of 86%, and negative predictive value of 43%. In the 15 patients for whom the presence or absence of cholesteatoma was correctly predicted, there were 2 cases where the reported locations of diffusion restriction did not correspond to the location of the cholesteatoma observed at surgery. CONCLUSION: On the basis of our retrospective study, we conclude that diffusion-weighted PROPELLER MRI is not sufficiently accurate to replace second look surgery at our institution.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".