Cost–utility analysis of open <i>versus</i> laparoscopic groin hernia repair: results from a multicentre randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: This study was a pragmatic economic evaluation carried out alongside a multicentre randomized controlled trial comparing laparoscopic with open groin hernia repair. The primary economic evaluation framework employed was a cost-utility analysis. METHODS: At 26 hospitals in the UK and Ireland, 928 patients with a groin hernia were assigned randomly to laparoscopic or open repair. Cost data were identified and measured both within and outwith the trial. Cost data were combined with quality-adjusted life years (QALYs) from the EQ-5D questionnaire to obtain cost-per-QALY ratios. RESULTS: The mean cost of laparoscopic hernia repair was pound1112.64, compared with pound788.79 for the open operation. The extra cost of pound323.85 in the laparoscopic group was mainly due to additional theatre time and increased equipment and sterilization costs. The estimated incremental cost per QALY of the laparoscopic over the open method was pound55 548.00 (95 per cent confidence interval pound47 216.00- pound63 885.00). CONCLUSION: While the results show that a high cost was incurred to produce an additional QALY by using laparoscopic over open hernia repair, sensitivity analyses show that there are specific situations in which laparoscopic repair may be a viable alternative, such as when reusable equipment is employed.
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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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".