Does Choice of Nasal Pack Matter? A Systematic Review and Meta‐Analysis of Merocel and Rapid Rhino
Bibliographic record
Abstract
OBJECTIVES: Epistaxis is one of the most common rhinological emergencies. Management often involves nasal packing when initial measures fail. This paper compares Rapid Rhino (RR) and Merocel in the management and prevention of epistaxis. DATA SOURCES: Studies from Embase, PubMed and Medline were included. REVIEW METHODS: A systematic review and subsequent meta-analysis were performed, pre-registered on PROSPERO and adhering to PRISMA guidelines. Studies were screened, followed by data extraction and risk of bias assessment. The meta-analysis was performed using Stata. Pain score effect size was based on raw means at packing removal, while rebleeding effect size used Freeman-Tukey's proportion. Pain score at removal and rebleeding requiring repacking was assessed. RESULTS: The systematic review yielded 4637 studies for screening, with 51 meeting inclusion criteria. In primary epistaxis, RR was associated with less pain upon insertion and removal. In the surgical setting, RR demonstrated superior hemostasis and greater patient comfort. The meta-analysis demonstrated that for post-surgical packing, RR was significantly less painful on removal than Merocel (Mean = 2.50 [1.72, 3.28] vs. 6.34 [5.58, 7.10]; p = 0.00). Similarly, for primary epistaxis, RR removal was significantly less painful than Merocel (Mean = 2.28 [0.95, 3.61] vs. 4.14 [3.31, 4.97]; p = 0.02). All tests of group differences for rebleeding demonstrated no significant differences between nasal packs. CONCLUSION: RR was found to be significantly less painful upon removal in primary and post-surgical epistaxis. Although the systematic review demonstrates that RR is associated with less bleeding than Merocel, the meta-analysis demonstrated no statistically significant difference.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".