Predictors of unexpected hospital admissions after outpatient endoscopic sinus surgery: retrospective review.
Bibliographic record
Abstract
BACKGROUND: Endoscopic sinus surgery (ESS) is typically performed on an outpatient basis in our centre. The purpose of this study is to determine the frequency of unexpected stays in patients who undergo ESS. This information would be useful to identify and to counsel these potential patients. METHODS: Retrospective chart review of 194 consecutive patients who had ESS during a 6-month span. Multivariate analysis was performed on 11 variables identified in the charts to determine whether any of the data predicted an unexpected admission. RESULTS: The unexpected rate of admission was 4.7%. Surgical complications causing admission occurred in 1% of the cases. Reasons for admissions included nausea and vomiting, hypotension, oxygen desaturation, headache, postoperative epistaxis, and observation for possible cerebrospinal fluid leak. None of the 11 variables that were examined showed statistical significance as independent predictors of unexpected admissions. However, two variables, the presence of comorbidities and the use of ondansetron, did approach statistical significance. CONCLUSIONS: Unexpected admissions following ESS are infrequent, and the reasons for admission are varied. In this study, although 9 of the 11 parameters evaluated showed no statistical significance as independent predictors of unexpected admissions, 2 did approach statistical significance. Intuitively, the presence of patient comorbidities would be expected to place the patient at greater risk of unexpected admission. Ondansetron is a potent antiemetic and is reserved for patients at risk or in those having severe symptoms. These patients would also be expected to have an increased risk of an unanticipated hospital stay.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".