Mortality following liver resection in US medicare patients: Does the presence of a liver transplant program affect outcome?
Bibliographic record
Abstract
BACKGROUND: Hepatic resection is a complicated procedure, at times associated with significant morbidity. Liver transplantation programs may improve outcomes following resective liver surgery at the institutional level by a number of means, including: availability of ancillary services and personnel, specialized critical care, and added surgical expertise. OBJECTIVES: To determine if the presence of a liver transplant program at a center improves outcomes following hepatic resection when compared to centers without an associated liver transplant program. METHODS: Using data from the national Medicare claims database, 30-day mortality following all hepatic resections performed over a 2-year period (1999, 2000) were studied. Regression techniques were used to assess the relationship between mortality at centers with an associated liver transplant program in comparison to those without, while controlling for potential confounding factors. RESULTS: The proportion of patients dying within 30 days among 4,661 patients that underwent hepatic resection was 6.65%. Factors that did increase the risk of dying after hepatic resection included: urgent or emergent surgery (vs. elective), primary liver cancer (vs. metastatic), male sex, increasing comorbidity score, low hospital volume, and extent of surgery. The presence of a liver transplant program within a center was not associated with any improvement in mortality. CONCLUSION: At an institutional level, the presence of a liver transplant program was not associated with decreased 30-day mortality following hepatic resection.
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 | 0.000 |
| 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 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".