Number of people travelling to Switzerland for assisted dying doubles in four years
Bibliographic record
Abstract
Background: Mortality for liver resection has remarkably improved owing to multiple factors. We sought to determine the impact of the various types of fellowship training on patient survival after liver resection. Methods: Patients who underwent hepatic resection between 1995 and 2004 in either the Calgary or Capital health regions (Edmonton) of Alberta, Canada, were identified using ICD-9 and -10 codes. Primary outcomes included in-hospital mortality and patient survival according to surgeon volume and training type (surgical oncology v. hepatobiliary v. others). Results: A total of 1033 patients underwent hepatic resection. Surgeon volume was not predictive of either in-hospital mortality (adjusted odds ratio 0.63, 95% confidence interval [CI] 0.32–1.20) or patient survival (unadjusted hazard ratio 1.11, 95% CI 0.82–1.51). Nonsignificance was also demonstrated for a surgeon’s type of fellowship training. Conclusion: The various modes of fellowship training do not appear to influence inhospital mortality or patient survival after 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 0.001 |
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".