Pain and haemorrhage are the most common reasons for emergency department use and hospital admission in adults following ambulatory surgery: results of a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Advances in healthcare delivery have allowed for the increase in the number of ambulatory surgery procedures performed in Canada. Despite these advances, patients return to hospital following discharge. However, the reason for unplanned healthcare use after ambulatory surgery in Canada is not well understood. AIMS: To examine unplanned healthcare use, specifically emergency department visit and hospital admissions, in the 3 days after ambulatory surgery in Ontario, Canada. METHODS: This population-based retrospective cohort study was conducted using de-identified administrative databases. Participants were residents in the province of Ontario, Canada; 18 years and older; and underwent common ambulatory surgical procedures between 2014 and 2018. The outcomes included emergency department (ED) visit and hospital admission. Incidence rates were calculated for the total cohort, for each patient characteristic and for surgical category. The odds ratios and 95% confidence intervals were calculated for each outcome using bivariate and multivariate logistic regression. RESULTS: 484,670 adults underwent select common surgical procedures during the study period. Patients had healthcare use in the first 3 days after surgery, with 14,950 (3.1%) ED visits and 14,236 (2.9%) admissions. The incidence of ED use was highest after tonsillectomy (8.1%), cholecystectomy (4.2%) and appendectomy (4.0%). Incidence of admissions was highest after appendectomy (21%). Acute pain (19.7%) and haemorrhage (14.2%) were the most frequent reasons for an ED visit and "convalescence following surgery" (49.2%) followed by acute pain (6.2%) and haemorrhage (4.5%) were the main reasons for admission. CONCLUSIONS: These findings can assist clinicians in identifying and intervening with patients at risk of healthcare use after ambulatory surgery. Pain management strategies that can be tailored to the patient, and earlier follow-up for some patients may be required. In addition, administrative decision-makers could use the results to estimate the impact of specific ambulatory procedures on hospital resources for planning and allocation of resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".