Successful Advance Directives through Quality Disease Management
Bibliographic record
Abstract
Recently there has been talk about the benefit of advance care planning. This is an issue which resurfaces from time to time, as is evident in recent New England Journal of Medicine articles and editorials (April 2010). It has also resurfaced in Canada in a recent document titled Advance Care Planning in Canada: National Framework for Consultation (Health Canada 2010). This document acknowledges that many of us believe in the value of advance directives, finding "that most of the general public (60-90%) is supportive of advance care planning. However, only 10-20% of the public in the US, Canada and Australia have completed an advance care plan of any kind" (Health Canada 2010: 6). In Muriel R. Gillick's editorial in the New England Journal Medicine, she strongly makes the point that few people complete advance directives and further states that "directives have been a resounding failure" (Gillick 2010: 1239). These statements, although not exhaustive on the subject, show that we have a problem translating the support for advance directives into actual plans.
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 | 0.068 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".