Cost of postoperative complications after general surgery at a major Canadian academic centre
Bibliographic record
Abstract
BACKGROUND: In a fiscally constrained health care environment, the need to reduce unnecessary spending is paramount. Postoperative complications contribute to hospital costs and utilization of health care resources. OBJECTIVE: The purpose of this observational study was to identify the cost associated with complications of common general surgery procedures performed at a major academic hospital in Toronto, Ontario. METHODS: The institutional National Surgical Quality Improvement Program database was used to identify complications in patients who underwent general surgical procedures at our institution from April 2015 to February 2018. A mix of elective and emergent cases was included: bariatric surgery, laparoscopic appendectomy, laparoscopic cholecystectomy, thyroidectomy, right hemicolectomy and ventral incisional hernia repair. The total cost for each visit was calculated by adding all the aggregate costs of inpatient care. Median total costs and the breakdown of cost components were compared in cases with and without complications. RESULTS: A total of 2713 patients were included. Nearly 6% of patients experienced at least one complication, with an incidence ranging from 1.1% after bariatric surgery to 23.8% after right hemicolectomy. The most common type of complication varied by procedure. Median total costs were significantly higher in cases with complications, with a net increase ranging from $2989 CAD (35% increase) after bariatric surgery to $10 459 CAD (161% increase) after ventral incisional hernia repair. CONCLUSION: Postoperative complications after both elective and emergent general surgery procedures add substantially to hospital costs. Quality improvement initiatives targeted at decreasing postoperative complications could significantly reduce costs in addition to improving patient outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".