Cost of open and laparoscopic distal gastrectomy: surgeon perceptions versus the reality of hospital spending
Bibliographic record
Abstract
BACKGROUND: Rising health care costs have led to increasing focus on cost containment and accountability from health care providers. We sought to explore surgeon awareness of supply costs for open and laparoscopic distal gastrectomy. METHODS: Surveys were sent in 2015 to surgeons at 8 academic hospitals in Toronto who performed distal gastrectomy for gastric adenocarcinoma. Respondents were asked to estimate the total cost, type and number of disposable equipment pieces required to perform open and laparoscopic distal gastrectomy. We determined the accuracy of estimates through comparisons with procedural invoices for distal gastrectomy performed between Jan. 1, 2011, and Dec. 31, 2015. All values are in 2015 Canadian dollars. RESULTS: Of the 53 surveys sent out, 12 were completed (response rate 23%). Surgeon estimates of total supply costs ranged from $500 to $3000 and from $1500 to $5000 for open and laparoscopic cases, respectively. Estimated supply costs for requested equipment ranged from $464 to $2055 for open cases and from $1870 to $2960 for laparoscopic cases. Invoices for actual equipment yielded a mean of $821 (standard deviation $543) (range $89-$2613) for open cases and $2678 (standard deviation $958) (range $835-$4102) for laparoscopic cases. Estimates of total cost were within 25% of the median invoice total in 1 response (9%) for open cases and 3 (27%) of those for laparoscopic cases. CONCLUSION: Respondents failed to accurately estimate equipment costs. The variation in true total costs and estimates of supply costs represents an opportunity for intraoperative cost minimization, efficient equipment selection and value-based purchasing arrangements.
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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 | 0.004 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".