Prospective cost analysis of laparoscopic vs. open pyeloplasty in children: Single centre contemporary evaluation comparing two procedures over a 1-year period
Bibliographic record
Abstract
INTRODUCTION: Laparoscopy in pediatric urological surgery continues to gradually gain acceptance. Since economic implications are of increasing importance in our cost-containment environment, few studies have compared the expense associated with open to laparoscopic approaches. We present a prospective comparative cost-analysis between the laparoscopic (LP) and open pediatric pyeloplasty (OP). METHODS: Over a period of a year (2007-2008), 54 consecutives pyeloplasties were performed. The "traditional" OP was performed in 33 patients and the remaining 21 children underwent LP. Costs were prospectively collected for each group and divided based on amounts incurred by all different departments involved: nursing, laboratory, diagnostic imaging, pharmacy and operative room. RESULTS: Overall, the average cost for a LP was CDN$6240 compared to CDN$5079 for an OP with a median hospital stay of 2 days (range OP: 1-18, LP: 1-7). The main difference was found in operative room expenses (OP: $2508 vs. LP: $3925). The higher cost could not be solely explained by the use of disposable items, which only subtracts $335 per procedure (23.6% of the cost difference between OP and LP). Length of time spent in the operating room was 1.2 hours longer for the LP and appears to be the main factor explaining the cost difference. CONCLUSION: Our findings show that at our institution, pediatric LP is more expensive than OP. This cost difference is mainly due to operating room time. For cost-containment purposes, efforts aimed at increasing efficiency in the operating room may help equalize both approaches.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".