Assessment of chronic rhinosinusitis with nasal polyps: Development of the Nasal Polyp Patient Assessment Scoring Sheet tool
Bibliographic record
Abstract
Background: Chronic rhinosinusitis (CRS) is a heterogeneous disorder with a wide range of validated subjective and objective assessment tools to assess disease severity. However, a comprehensive and easy-to-use tool that integrates these measures for determining disease severity and response to treatment is still obscure. The objective of this study was to develop a standardized assessment tool that facilitates diagnosis, uniform patient monitoring, and comparison of treatment outcomes between different centers both in routine clinical practice and in research. Methods: To develop this tool, published literature on assessment tools was searched on various databases. A panel of 12 steering committee members conducted an advisory board meeting to review the findings. Specific outcome measures to be included in a comprehensive assessment tool and follow-up sheet were then collated following consensus approval from the panel. The tool was further validated for content and revised with expert recommendations to arrive at the finalized Nasal Polyp Patient Assessment Scoring Sheet (N-PASS) tool. Results: The N-PASS tool was developed by integrating the subjective and objective measures for CRS assessment. Based on expert opinions, N-PASS was revised to be used as an easy-to-use guidance tool that captures patient-reported and physician-assessed components for comprehensively assessing disease status and response to treatment. Conclusion: The N-PASS tool can be used to aid in the diagnosis and management of CRS cases with nasal polyps. The tool would also aid in improved monitoring of patients and pave the way for an international disease registry. Level of evidence: Oxford Level 3.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".