Economic impact of powered stapler in video-assisted thoracic surgery lobectomy for lung Cancer in a Chinese tertiary hospital: a cost-minimization analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To assess the economic impact of powered stapler use in video-assisted thoracic surgery (VATS) lobectomy for lung cancer in a Chinese tertiary care hospital. METHODS: This study identified 388 patients who received VATS lobectomy using the ECHELON powered stapler (n = 296) or the ECHELON manual stapler (n = 92) for lung cancer in a Chinese tertiary hospital. Multiple generalized linear regression analyses were conducted using data on hospital costs and patient characteristics to develop predictive equations for hospital costs in a cost-minimization analysis (CMA) model comparing hospital costs associated with the ECHELON powered stapler and the ECHELON manual stapler. CMA model was used to conduct scenario analysis to compare the ECHELON powered stapler with another manual stapler (Victor Medical). RESULTS: The multiple generalized linear regression analyses identified that using the ECHELON powered stapler in VATS lobectomy for lung cancer was associated with significantly lower drug costs than using the ECHELON manual stapler (coefficient - 0.256, 95% confidence interval: - 0.375 to - 0.139). The CMA model estimated that the ECHELON powered stapler could save hospital costs by ¥1653 when compared with the ECHELON manual stapler (¥65,531 vs. ¥67,184). The use of the ECHELON powered stapler also saved hospital costs by ¥4411 when compared with the Victor Medical manual stapler (¥65,531 vs. ¥69,942) in the scenario analysis. CONCLUSIONS: Compared to the two manual staplers used for VATS lobectomy for lung cancer in a Chinese tertiary hospital, the ECHELON powered stapler had 100% probability to save total hospital costs under present prices of the three staplers according to the CMA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it