Postoperative management in the prevention of complications after septoplasty
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The purpose of this article is to assess the relative rates of septal hematomas, synechiae, and septal perforations associated with methods commonly used to manage the nasal septum after septoplasty. As a secondary objective, we assessed the relative contribution of each method of septal management with respect to pain and patient discomfort. STUDY DESIGN: Systematic review. METHODS: A systematic literature search was performed for all relevant English randomized controlled, cohort, or case-control trials. Inclusion criteria included any study that assessed postoperative septal hematoma, perforation, or synechiae formation and reported on these outcomes regardless of method of septoplasty. Two authors independently extracted study information and analyzed all included articles for bias. RESULTS: A total of 279 studies were identified, with 17 meeting the inclusion criteria. The majority of the studies had a high risk of bias that prevented the performance of a meta-analysis. Eight studies provided data on postoperative pain associated with different techniques, and quilting sutures were found to be significantly less painful than both nasal packing and septal splints. CONCLUSIONS: Due to the low level of evidence and the high bias of the studies, the results of this systematic review fail to demonstrate a clear benefit among any of the postseptoplasty treatment techniques. However, the results do demonstrate that septal sutures are associated with less postoperative pain versus the other methods of septal management in this review.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".