Advance Care Planning (ACP) vs. Advance Serious Illness Preparations and Planning (ASIPP)
Bibliographic record
Abstract
COVID-19 has highlighted the reality of an impending serious illness for many, particularly for older persons. Those faced with severe COVID-19 infection or other serious illness will be faced with decisions regarding admission to intensive care and use of mechanical ventilation. Past research has documented substantial medical errors regarding the use or non-use of life-sustaining treatments in older persons. While some experts advocate that advance care planning may be a solution to the problem, I argue that the prevailing understanding and current practice of advance care planning perpetuates the problem and results in patients not receiving optimal patient-centered care. Much of the problem centers on the framing of advance care planning around end of life care, the lack of use of decision support tools, and inadequate language that does not support shared decision-making. I posit that a new approach and new terminology is needed. Advance Serious Illness Preparations and Planning (ASIPP) consists of discrete steps using evidence-based tools to prepare people for future clinical decision-making in the context of shared decision-making and informed consent. Existing tools to support this approach have been developed and validated. Further dissemination of these tools is warranted.
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 | 0.031 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".