Precision medicine: why surgeons deviate from “appropriateness criteria” in the management of chronic rhinosinusitis and effects on outcomes
Bibliographic record
Abstract
BACKGROUND: In uncomplicated chronic rhinosinusitis (CRS), a consensus regarding appropriate medical therapy (AMT) before surgical intervention has been published in the form of "appropriateness criteria" for endoscopic sinus surgery (ESS). We sought to determine why tertiary surgeons may deviate from the suggested criteria and evaluated whether those deviations result in change in outcomes. METHODS: Patients with uncomplicated CRS were prospectively enrolled over the course of 1 year. The 22-item Sino-Nasal Outcomes Test (SNOT-22), a general health outcome out of 100, and a physician form, indicating management pathway and decision making, was completed at each visit over a 6-month follow-up period. A descriptive analysis was used to quantify reasons for veering from the "appropriateness criteria," and repeated linear regression modeling was used to measure whether compliance impacted SNOT-22, general health, and Lund-Kennedy (LK) scores over the period of study. RESULTS: One hundred fifty-five patients were enrolled. Sixty-eight percent followed the appropriate management pathway based on their presentation and the suggested criteria. Disparate reasons were documented for deviation in the other 32%, and, despite establishing several predictive categories, "other" was the most common reason, with various explanations well documented. SNOT-22, general health, and LK scores were not statistically impacted by compliancy status (p > 0.05). CONCLUSION: The suggested "appropriateness criteria" predict a management pathway for the majority of CRS patients. However, in a tertiary sinus center, surgeons may deviate from that model with a significant minority of their patients, for multiple reasons, without causing a change in outcomes.
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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 | 0.054 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".