The utility of routine polyp histopathology after endoscopic sinus surgery
Bibliographic record
Abstract
BACKGROUND: Routine histopathological assessment is standard practice for nasal polyp specimens obtained during endoscopic sinus surgery (ESS) for chronic rhinosinusitis (CRS). Retrospective studies suggest that routine histopathology of nasal polyps shows few unexpected diagnoses that alter patient management. Our objective was to study the use of routine pathological analysis, and its cost to the healthcare system, in a prospective manner. METHODS: A multicenter prospective assessment was performed from data collected between 2007 and 2013. Only cases of patients undergoing ESS for bilateral CRS were included. We excluded unilateral disease cases, and cases in which diagnoses other than polyps were suspected either preoperatively or intraoperatively. We then compared the preoperative diagnosis with the final histopathology and identified the rate of unexpected pathologies. A cost analysis was performed. RESULTS: Only 4 of 866 pathological specimens were identified as having a clinically significant unexpected diagnosis. All unexpected pathologies in this series were benign. These 4 cases account for 0.46% of all specimens reviewed. This translates to a number needed to screen of 217 cases of bilateral CRS to discover 1 unexpected pathology. The associated cost for making an unexpected diagnosis was $19,192.73. CONCLUSION: Routine histopathology of nasal polyps in ESS for bilateral CRS with polyps yields few unexpected and management-altering diagnoses. It carries a significant cost to the healthcare system. In cases of bilateral CRS with no other concerning clinical features, clinicians should exercise judgment in submitting polyp specimens for pathology rather than routinely sending polyps for histopathologic analysis.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".