Population-based review of the outcomes following hepatic resection in a Canadian health region.
Bibliographic record
Abstract
BACKGROUND: Higher hospital and surgeon volumes have been associated with improved outcomes following hepatic resection; however, there appear to be additional factors that also play a role. The objective of our study was to examine the outcomes following hepatic resection over the past 13 years in a large urban Canadian health region. METHODS: We used administrative procedure codes to identify all patients from 1991/92 to 2003/04 who underwent a hepatic resection in the Calgary health region, which has a referral base of about 1.5 million people. The primary outcome was operative mortality, defined as death before discharge. RESULTS: There were 424 hepatic resections performed in the stated time period. Annual volume was stable until 2000, when it increased substantially. This corresponded to the formation of a multidisciplinary group that provided care to these patients. There were 25 deaths over the study period for a mean mortality of 5.9%. The mean length of stay in hospital was 14.6 (median 10) days. Over time, however, mortality steadily decreased. This corresponded to a concomitant increase in the volume of hepatic resections performed. CONCLUSION: Over the past 13 years, the number of hepatic resections performed has increased; there has been a corresponding improvement in mortality rates. The improved rates are likely the result of multiple factors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".