A Clinical Decision Analysis for Use of Antibiotic Prophylaxis for Nonabsorbable Nasal Packing
Bibliographic record
Abstract
Objective Nonabsorbable nasal packing is often placed for the treatment of epistaxis or after sinonasal or skull base surgery. Antibiotics are often prescribed to prevent toxic shock syndrome (TSS), a rare, potentially fatal occurrence. However, the risk of TSS must be balanced against the major risk of antibiotic use, specifically Clostridium difficile colitis (CDC). The purpose of this study is to evaluate in terms of cost‐effectiveness whether antibiotics should be prescribed when nasal packing is placed. Study Design A clinical decision analysis was performed using a Markov model to evaluate whether antibiotics should be given. Setting Patients with nonabsorbable nasal packing placed. Methods Utility scores, probabilities, and costs were obtained from the literature. We assess the cost‐effectiveness of antibiotic use when the risk of community‐acquired CDC is balanced against the risk of TSS from nasal packing. Sensitivity analysis was performed for assumptions used in the model. Results The incremental cost‐effectiveness ratio for antibiotic use was 334,493 US dollars (USD)/quality‐adjusted life year (QALY). Probabilistic sensitivity analysis showed that not prescribing antibiotics was cost‐effective in 98.0% of iterations at a willingness to pay of 50,000 USD/QALY. Sensitivity analysis showed that when the risk of CDC from antibiotics was greater than 910/100,000 or when the incidence of TSS after nasal packing was less than 49/100,000 cases, the decision to withhold antibiotics was cost‐effective. Conclusions Routine antibiotic prophylaxis in the setting of nasal packing is not cost‐effective and should be reconsidered. Even if antibiotics are assumed to prevent TSS, the risk of complications from antibiotic use is of greater consequence. Level of Evidence 3a
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".