Unplanned hospital admission in children undergoing day-case surgery
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Unplanned hospital admission is a measure of quality of care in the setting of day-case surgery. We set out to identify the incidence and causes of unplanned hospital admission in a paediatric day-case unit. METHODS: A retrospective survey to determine the incidence and causes of unplanned hospital admissions in children undergoing day-case surgery. The survey covered the period from January 1996 until December 1999 inclusive in a university affiliated children's hospital. This hospital is the second largest paediatric referral centre in Ireland with total admissions across all specialities during the study period of 42 738. RESULTS: During the study period 10 772 children underwent day-case surgery, of whom 242 (2.2%) experienced unplanned hospital admission. The reasons for admission were surgical 146 (54%), anaesthetic 44 (16%), social 38 (14%), medical 31 (11%) and unclassified 10 (4%). Pain, surgical complications and/or further management, admission for observation, extensive surgery and oozing were the commonest surgical reasons. Postoperative nausea and vomiting, anaesthetic-related complication and somnolence were the commonest anaesthetic causes responsible for admission. Surgery performed after 15:00 h was an important factor associated with admission for social reasons. Orthopaedic surgery accounted for the largest absolute number of unplanned admissions with 61 (25%), followed by urology with 46 (19%) and general surgery with 46 (19%). However, measured as percentage of caseload, urology had the highest proportion of unplanned hospital admissions. CONCLUSION: This study demonstrated that the incidence and causes of unplanned hospital admission following day-case surgery in children are similar to those for adults.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".