Evaluating the Reproducibility of Sinus Lavages with a Saline Solution Administered Directly in the Maxillary Sinus of Patients after Endoscopic Sinus Surgery
Bibliographic record
Abstract
INTRODUCTION: Chronic sinusitis is recognized as having a strong inflammatory component, and failures of endoscopic sinus surgery (ESS) are frequently attributed to persistent inflammation. A test that would allow rhinologists to evaluate the inflammatory state of a patient's sinuses would be helpful to evaluate cases refractory to therapy, determine appropriate medical therapy, and monitor the response to therapy. OBJECTIVES: The goal of this preliminary research is to assess the optimal method of collection and the reproducibility and specificity of sinus lavages. METHOD: Twelve patients who had undergone ESS were recruited. They were divided into two groups according to the persistence of their symptoms and the recurrence of acute sinusitis after ESS. The subjects were seen twice. Three successive lavages were collected from each maxillary sinus and were analyzed by cell count. RESULTS: Intrasession cell counts were most reproducible (Spearman rank correlation .7 for eosinophils and .6 for neutrophils) for the second lavage. Intersession cell counts were highly reproducible for eosinophils (r = .7) for the second lavage. The two-tailed t-test did not reveal any statistically significant differences between the good and the poor outcome groups. CONCLUSION: Assessment of eosinophil cell counts on sinus lavage is a feasible and reproducible method to evaluate the inflammatory state of a patient's sinuses in patients who have undergone ESS.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".