Economic impact of an enhanced recovery pathway for oesophagectomy
Bibliographic record
Abstract
BACKGROUND: Data are lacking to support the cost-effectiveness of enhanced recovery pathways (ERP) for oesophagectomy. The aim of this study was to investigate the impact of an ERP on medical costs for oesophagectomy. METHODS: This study investigated all patients undergoing elective oesophagectomy between June 2009 and December 2011 at a single high-volume university hospital. From June 2010, all patients were enrolled in an ERP. Clinical outcomes were recorded for up to 30 days. Deviation-based cost modelling was used to compare costs between the traditional care and ERP groups. RESULTS: A total of 106 patients were included (47 traditional care, 59 ERP). There were no differences in patient, pathological and operative characteristics between the groups. Median length of hospital stay (LOS) was lower in the ERP group (8 (interquartile range 7-18) days versus 10 (9-18) days with traditional care; P = 0·019). There was no difference in 30-day complication rates (59 per cent with ERP versus 62 per cent with traditional care; P = 0·803), and the 30-day or in-hospital mortality rate was low (3·8 per cent, 4 of 106). Costs in the on-course and minor-deviation groups were significantly lower after implementation of the ERP. The pathway-dependent cost saving per patient was €1055 and the overall cost saving per patient was €2013. One-way sensitivity analysis demonstrated that the ERP was cost-neutral or more costly only at extreme values of ward, operating and intensive care costs. CONCLUSION: A multidisciplinary ERP for oesophagectomy was associated with cost savings, with no increase in morbidity or mortality.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".