Effect of Hospital Characteristics on the Quality of Laparoscopic Gastrectomy in Japan
Bibliographic record
Abstract
BACKGROUND: Laparoscopic gastrectomy (LG) is becoming more widely indicated, although its application has not been investigated sufficiently in community-based gastrointestinal research because the small number of gastric cancers in western countries might have limited its use. However, concerns have been raised regarding variations in the quality of care with LG. To contribute to improving the efficient utilization of costly surgical innovations, we determined the impact of hospital characteristics on LG care. METHODS: Among 3,914 LG patients in 58 academic and 200 community hospitals between 2006 and 2008, we examined patient demographics, comorbidity, complications, partial or total gastrectomy, care process, hospital patient volume, hospital ownership and teaching status, and fiscal year. Hospital LG volume was divided into three quintile categories (lower volume, LV; intermediate volume, IV; or higher volume, HV) that consisted of an approximately equal number of patients. Dependent variables were operating time (OT), length of stay (LOS) and total charge (TC). The impact of hospital characteristics on these variables was assessed using multivariate analysis. RESULTS: Twenty-seven academic hospitals out of 193 LV hospitals treated 271 (21%) LG patients, 20 of 44 IV hospitals treated 596 (47%), and 11 of 21 HV hospitals treated 748 (55%). Although mortality or complications did not vary significantly between LV, IV and HV hospitals, the latter were associated with longer OT or LOS and more TC. More blood transfusions were required in HV hospitals once indicated. Hospital ownership or teaching status did not explain the variation in complications. Teaching and national hospitals consumed more resources, and municipal and private hospitals reduced OT more than national hospitals. CONCLUSIONS: A volume-quality relationship was recognized. As intraoperative transfusion prolongs OT and results in more complications, clinical societies or policy makers should introduce this new technique concurrently with quality improvement initiatives that aim to reduce unnecessary OT at targeted institutions. Hospitals varied in terms of LOS and TC, therefore, policy makers should also monitor resource utilization to enhance the efficiency of LG care under restrictive fiscal policies.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".