Promoting excellence in end‐of‐life care in ALS
Bibliographic record
Abstract
The type and quality of end-of-life care varies greatly in ALS; the time to initiate end-of-life care is not defined, and decision making is hampered by logistical and financial barriers. There has been no systematic review of these issues in ALS. The goals of this initiative are to: 1) improve end-of-life care for patients with ALS and families based on what limited evidence is available; 2) increase awareness, interest, and debate on the end-of-life care in ALS; and 3) identify areas needed for new prospective clinical research. The ALS Peer Workgroup reviewed the literature and 1) identified the current state of knowledge, 2) analysed the gaps in care, and 3) provided recommendations for standard of care and future research. It was shown that areas of investigation are needed on the incorporation of an interdisciplinary approach to care in ALS that includes: psychosocial evaluation and spiritual care; the use of validated instruments to assess patient and caregiver quality of life; and the establishment of proactive caregiver programs. Several public policy changes that will improve coverage for medical care, hospice, and caregiver costs are also reviewed. More clinical evidence is needed on how to provide optimal end-of-life care specifically in ALS.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".