Nasal spray adherence after sinus surgery: problems and predictors.
Bibliographic record
Abstract
OBJECTIVES: To assess patient adherence to nasal spray regimens after endoscopic sinus surgery (ESS) and to study factors that predict adherence. METHODS: A three-arm, randomized, blinded, controlled trial was conducted at a tertiary care academic hospital, studied via a prospective longitudinal survey, of 60 consecutive chronic rhinosinusitis patients managed with ESS and started on one of three postoperative nasal spray regimens. Structured telephone interviews were conducted after surgery over a 12-month period using a validated questionnaire that assessed both spray adherence and barriers to adherence. Patient demographics, time post-ESS, preoperative Sino-Nasal Outcome Test (SNOT) scores, Lund-Mackay scores, adherence risk factors, and polyp grades were used as covariates with logistic regression. RESULTS: Overall, 57.4% of patients were nonadherent. Logistic regression showed that preoperative SNOT scores (p = .018, 95% CI = 0.84-0.98), time post-ESS (p = .016, 95% CI = 1.02-1.22), and the presence of an adherence risk factor (p = .03, 95% CI = 1.18-26.99) significantly predicted whether a patient was adherent and correctly classified 70.4% of all patients. Age, gender, and nasal spray regimen did not predict adherence (p > .05). CONCLUSION: The majority of patients were nonadherent to post-ESS nasal sprays, irrespective of which nasal spray regimen they were on. Preoperative SNOT scores, time post-ESS, and the presence of an adherence risk factor predicted adherence. With this knowledge, otolaryngologists can selectively employ strategies to improve adherence in high-risk patients and possibly improve ESS 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".