International changes in end-of-life practices over time: a systematic review
Bibliographic record
Abstract
BACKGROUND: End-of-life policies are hotly debated in many countries, with international evidence frequently used to support or oppose legal reforms. Existing reviews are limited by their focus on specific practices or selected jurisdictions. The objective is to review international time trends in end-of-life practices. METHODS: We conducted a systematic review of empirical studies on medical end-of-life practices, including treatment withdrawal, the use of drugs for symptom management, and the intentional use of lethal drugs. A search strategy was conducted in MEDLINE, EMBASE, Web of Science, Sociological Abstracts, PAIS International, Worldwide Political Science Abstracts, International Bibliography of the Social Sciences and CINAHL. We included studies that described physicians' actual practices and estimated annual frequency at the jurisdictional level. End-of-life practice frequencies were analyzed for variations over time, using logit regression. RESULTS: > 1000, p < 0.001 for all). Regression analyses showed increased use of opiates and sedatives over time (p < 0.001), which could reflect more intense symptom management at the end of life, or increase in these drugs to intentionally cause patients' death. CONCLUSION: The use of opiates and sedatives appears to have significantly increased over time between 1990 and 2010. Better distinction between practices with different legal status is required to properly interpret the policy significance of these changes. Research on the effects of public policies should take a comprehensive look at trends in end-of-life practice patterns and their associations with policy changes.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".