97 Review of data from the 2016 official reports of the dutch termination of life on request and assisted suicide act and oregons death with dignity act
Bibliographic record
Abstract
Of 6091 notified cases of physician assisted suicide (PAS)/euthanasia (PAE) in the Netherlands, 3840 (63%) were 70 years or over: the proportion rises to 86% (5248 PAS/PAE deaths) if over-60s are included. Oregon’s official report on their PAS law, which requires a prognosis of <6 months, shows 71% of PAS deaths in 2016 were aged 65 or over (median 73 years). Both legislatures show rising incidences of PAS/PAE since legislation came into force. In the Netherlands the annual numbers of deaths initially remained stable and fears of rising death rates were thought groundless. After 2007, the annual numbers of deaths began to rise steeply. In 2016, 1 in every 25 deaths the result of legalised PAS or PAE. A law like Holland’s 2001 Act would probably result in around 21 000 such deaths annually in England and Wales. In 2010, of 3,136 Dutch PAS/PAE notified, 2781 (89%) were related to cancer, cardiovascular and neurological disorders and 11 per cent to other conditions. By 2016 a rising proportion (17%) related to multiple geriatric syndromes, dementia (n=141), psychiatric disorders (n=60), and other conditions. Statistics Netherlands data confirms this trend. Extension of euthanasia caused psychiatrist Boudewijn Chabot, whose prosecution preceded Dutch legislation, to express concern, writing that the foundation of the law has been gradually eroded so that now it ‘does not provide protection to people with dementia and psychiatric problems. Non-assisted suicide rates have not fallen where PAS/PAE is legalised, but the introduction of PAS seemingly induces more self-inflicted deaths than it inhibits. WHO data shows higher-than-average suicide rates per 1 00 000 population (2015) of 20.5 in Belgium, 15.1 in Switzerland, 12.3 in Canada, 11.9 in the Netherlands and 11.1 in Luxembourg – global average was 10.7; UK was 8.5.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.039 | 0.050 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".