Cost-comparison analysis of diffusion weighted magnetic resonance imaging (DWMRI) versus second look surgery for the detection of residual and recurrent cholesteatoma
Bibliographic record
Abstract
BACKGROUND: Cholesteatoma is a destructive, erosive growth of keratinizing squamous epithelium in the middle ear cleft. Following treatment with a canal wall-up (CWU) tympanomastoidectomy, surveillance of residual and recurrent disease has traditionally been achieved through a second look tympanotomy following the initial procedure. Historically, MRI sequences have been inadequate at differentiating between granulation tissue, inflammation, and cholesteatoma. Recent literature has shown diffusion-weighted magnetic resonance imaging (DWMRI) to be a viable alternative to second look surgery for the detection of residual or recurrent disease. The goal of the present study was to perform a cost analysis of DWIMRI versus second look surgery in the detection of residual or recurrent cholesteatoma following combined approach tympanomastoidectomy. METHODS: A probabilistic decision tree model was generated from a literature review to compare traditional second look surgery with DWMRI. Cost inputs were obtained from the Ontario Case Costing Initiative, the Ontario Health Insurance Plan (OHIP) schedule of benefits. Costs were reported in Canadian dollars and a payer perspective was adopted. A probabilistic sensitivity analysis was performed. RESULTS: According to the probabilistic sensitivity analysis, mean cost difference of traditional second look tympanotomy versus echo planar imaging (EPI) DWMRI was $180.27CAD, 95%CI [$177.32, $188,32] in favour of second-look tympanotomy. However, mean cost difference of traditional second look tympanotomy versus non-EPI DWMRI was $390.66CAD, 95%CI [$381.52, $399.80] in favour of non-EPI DWMRI. CONCLUSIONS: Diffusion-weighted MRI, specifically non-EPI sequences, are a viable cost-saving alternative to second-look tympanotomy in the setting of detecting residual or recurrent cholesteatoma.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.015 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".