The usefulness of routine histopathology of bilateral nasal polyps – a systematic review, meta-analysis, and cost evaluation
Bibliographic record
Abstract
Abstract Background Controversy regarding the usefulness of routine histopathological examination of bilateral nasal polyps removed during endoscopic sinus surgery to identify occult diagnoses still exists. There is a paucity of high-level evidence in the literature. Methods A systematic review and meta-analysis was conducted. Two independent reviewers were used. Pooled proportions and numbers needed to screen were calculated. A cost per life year model was generated based on varying survival benefits and compared to other Canadian screening programs to provide financial context. Results Six studies (n = 3772 patients) were included. Of the 3772 patients, 3751 had a pre-operative clinical and post-operative pathological diagnosis of inflammatory nasal polyps. Agreement proportion was 99.44 %. There were 18 unexpected benign and three unexpected malignant diagnoses identified. This translated to a proportion of 0.48 and 0.08 % respectively. Number needed to screen was 210 and 1258 respectively. Pooled proportion for expected findings using a random effect model was 0.99 (95 % CI = 0.99–1). Pooled proportion for unexpected benign findings using a random effect model was 0.00522 (95 % CI = 0.00133–0.01). Pooled proportion for unexpected malignant findings using a random effect model was 0.00107 (95 % CI = 0.000147–0.00283). The cost to pick up one unexpected benign diagnosis was $14557.2. The cost to pick up 1 unexpected malignant diagnosis was $87204.56. Cost per quality life year calculated ranged from 3211.83 to $64677.58 based on varying assumptions on the survival benefits of identifying an unexpected malignancy. Conclusions Routine pathological examination in screening for neoplasia may be low yield, however, no compelling evidence was found to cease such practice. Surgeons should exercise individual judgment in requesting routine examination.
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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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".