The impact of surgical wait time on patient reported outcomes in sinus surgery for chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: In many developed countries, wait times for elective surgery are increasing. Among these elective surgeries is endoscopic sinus surgery (ESS) performed for treatment of chronic rhinosinusitis (CRS). Little is known about the impact of wait times on patients' surgical outcomes. The purpose of this study was to evaluate the association between patients' wait times and postoperative patient-reported outcomes. METHODS: This study was based on a prospectively recruited longitudinal cohort of patients booked for ESS for the treatment of medically-refractory CRS in Vancouver, Canada. Patients were recruited between September 2012 and December 2016. All participants completed the Sino-Nasal Outcome Test (SNOT-22) preoperatively and 6 months postoperatively. The primary outcome measure was participants' change in SNOT-22 score. A regression model measured the association between patient-reported outcome, wait time, and potential confounders. RESULTS: The study included 150 participants. The mean surgical wait time was 32 weeks. The mean preoperative SNOT-22 score was 40.0. The improvement in SNOT-22 scores following ESS was 18 points. Regression analysis found no association between wait time for ESS and the change in SNOT-22 scores after surgery (p = 0.42). Only preoperative SNOT-22 score correlated with outcome scores. CONCLUSION: There was no association between the gains in health-related quality of life and the length of time participants waited for surgery. The largest gains in health were concentrated among participants with the highest symptom burden, irrespective of wait time. This result suggests that it may be safe to triage patients based on symptom severity as an approach to maximizing the population's overall health.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".