Appropriateness Criteria for Surgery in the Management of Adult Recurrent Acute Rhinosinusitis
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Endoscopic sinus surgery (ESS) is frequently performed for recurrent acute rhinosinusitis (RARS). Appropriate indications for surgery among patients with RARS have not yet been rigorously determined. The objective of this study was to define appropriateness criteria for ESS in the management of adult RARS. STUDY DESIGN: Application of RAND-UCLA appropriateness methodology. METHODS: A panel of nine multidisciplinary experts in RARS was formed to evaluate RARS scenarios generated from current evidence. The panel completed two rounds of a modified Delphi-ranking process and a teleconference. RESULTS: A total of 32 clinical scenarios were ranked in each round. For adult patients with RARS, ESS can appropriately be offered as a treatment option when patients experience ≥ four annual episodes, and there is confirmation of at least one episode via computed tomography or nasal endoscopy, and the patient and clinician jointly participate in shared decision making, and the patient has either failed a trial of topical nasal steroids or experienced RARS-related productivity loss. CONCLUSIONS: This study has defined appropriateness criteria for ESS as a management option for adult patients with RARS. These criteria are intended to represent a minimum threshold for which ESS should be considered in the treatment of RARS and do not suggest that all patients who meet these criteria should undergo surgery. These criteria may serve as a baseline set of indications for ESS in patients with RARS. LEVEL OF EVIDENCE: NA Laryngoscope, 129:37-44, 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".