Is nasal packing necessary after septoplasty? A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Nasal packing is routinely used after septoplasty because it is believed to decrease risk of postoperative bleeding, hematomas, and adhesions. Multiple studies have shown, however, that there are numerous complications associated with nasal packing. The purpose of this work was to perform a meta-analysis on the existing literature to evaluate the role of nasal packing after septoplasty. METHODS: Two independent reviewers conducted a literature search using EMBASE, OVID, Medline, PubMed, Google scholar, Cochrane Library, and reference list review from 1966 to August 2010 to identify studies assessing nasal packing after septoplasty. All papers were reviewed for study design, results, and were assigned an Oxford level of evidence grade, Detsky score, and Methodological Index for Nonrandomized Studies (MINORS) score. RESULTS: Sixteen papers were identified that met the inclusion criteria. Eleven papers were randomized control trials, 3 were prospective, and 2 were retrospective studies. Nasal packing did not show benefit in reducing postoperative bleeding, hematomas, septal perforations, adhesions, or residual deviated nasal septum. There was, however, an increase in postoperative infections. Two studies using fibrin products as nasal packing showed a decreased bleeding rate. CONCLUSION: Nasal packing after septoplasty does not show any postoperative benefits. Fibrin products show a possibility of decreasing postoperative bleeding. Routine use of nasal packing after septoplasty is not warranted. This is the first meta-analysis conducted on this topic.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.050 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".