Drug‐eluting implants in chronic rhinosinusitis: an evidence‐based review with recommendations
Bibliographic record
Abstract
BACKGROUND: Drug-eluting implants are becoming increasingly popular in the treatment of chronic rhinosinusitis (CRS). A previous attempt to make an evidence-based recommendation was hindered by limited evidence and experience with these implants. Since that time, the body of literature discussing drug-eluting implants has grown rapidly. The objective of this study was to review drug-eluting implants designed for use in the sinonasal cavity through an evidence-based review with recommendations. METHODS: A systematic review of the literature was performed using PubMed, EMBASE, Cochrane Review, and gray literature databases from January 1990 through February 2019 to examine drug-eluting implants used in CRS. Benefit-harm assessments, value judgments, and recommendations were made based on the available evidence. Study exclusion criteria included studies unavailable in English and non-endoscopic sinus surgeries. All authors agreed on recommendations through an iterative process. RESULTS: Thirty-one studies were included in the final analysis. Absorbable drug-eluting implants achieved a high aggregate grade of evidence (A), with a recommendation for their use to be considered in carefully selected patients. Nonabsorbable drug-eluting implants were recommended against due to minimal evidence supporting clinical efficacy. Antibiotic and alternative drug-eluting implants lack sufficient evidence for recommendations. CONCLUSION: Absorbable steroid-eluting implants are recommended for carefully selected patients with CRS. Additional research to define appropriate patient selection is needed.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".