Raman Spectroscopy for Inverted Papilloma: A Proof‐of‐Concept Study
Bibliographic record
Abstract
Inverted papillomas are tumors of the sinonasal tract with a propensity to recur. Raman spectroscopy can potentially identify inverted papillomas from other tissue based on biochemical signatures. A pilot study comparing Raman spectroscopy to histopathology for 3 types of sinonasal tissue was performed. Spectral data of biopsies from patients with normal sinonasal mucosa, chronic rhinosinusitis, and inverted papillomas are compared to histopathology using principal component analysis and linear discriminant analysis after data preprocessing. A total of 18 normal, 15 chronic rhinosinusitis, and 18 inverted papilloma specimens were evaluated. The model distinguished normal sinonasal mucosa, chronic rhinosinusitis, and inverted papilloma tissue with an overall accuracy of 90.2% (95% confidence interval, 0.86-0.94). In conclusion, Raman spectroscopy can distinguish inverted papilloma, normal sinonasal mucosa, and chronically rhinosinusitis tissue with acceptable accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".