Prognosis of Chronic Rhinosinusitis With Nasal Polyps Using Preoperative Eosinophil/Basophil Levels and Treatment Compliance
Bibliographic record
Abstract
Background Patients with chronic rhinosinusitis with nasal polyps (CRSwNP) have a high risk of disease recurrence and revision surgery. The ability to predict a polyp recurrence in this patient population is critical in order to provide adequately tailored postoperative management. Objective We aim to explore the role of appropriate postoperative care in the prognosis of CRSwNP patients in relation to preoperative eosinophil and basophils levels. Methods This was a retrospective case series; data were collected for 102 CRSwNP patients over a period of 15 months after surgery. Baseline eosinophil and basophil levels were compared between patients with and without polyp recurrences. The analysis was then stratified based on clinical diagnosis, comorbidities (atopy, asthma, and aspirin allergy), a single versus multiple episodes of sinonasal polyp recurrences, and medication adherence. Results Of the 102 included patients, 65 (63.7%) of the patients experienced no recurrences, 26 (25.5%) experienced a single episode of recurrence, and 11 (10.8%) experienced multiple recurrences. Mean baseline eosinophil count and percentage of total white blood cells were significantly higher in the multiple recurrences group (0.70 × 10 9 /L and 10%) compared with the no recurrences group (0.36 × 10 9 /L and 5%). Adherence to prescribed medical therapy prior to the first episode of recurrence was significantly lower for the single exacerbations group (42.3%) than the multiple recurrences group (88.9%). Conclusions Patients with multiple recurrences of nasal polyps had significantly higher baseline eosinophil counts and significantly higher medication adherence compared to single exacerbations of nasal polyps. Single exacerbations may not reflect true failures of surgery but rather a failure of postoperative medical care. Basophil levels were inadequate to predict polyp recurrence rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".