The ALS Patient Care Database: Insights into End-of-Life Care in ALS
Bibliographic record
Abstract
OBJECTIVE: To study clinical practices and patient outcomes near the end of life in amyotrophic lateral sclerosis (ALS). BACKGROUND: Patients, families, and healthcare providers face several dilemmas in selecting and delivering care near the end of life in ALS. Published data on clinical practices and their benefits during end-of-life care for ALS patients consist of anecdotal reports based on small case series or individual case reports. METHODS: Data were obtained from 1014 American and Canadian patients with ALS who died while participating in a large observational registry (the ALS Patient Care Database) during the past four years. Following death, a caregiver or family member provided data for each patient using a standard questionnaire. Data were principally generated through American and Canadian ALS multidisciplinary centers of excellence. RESULTS: Most patients died peacefully (90.7%) and 62.4% died in a hospice-supported environment. Advance directives were in place for 88.9% of patients and were followed in 96.8%. Among the 67 patients who exhibited distress in the dying process, symptoms included breathing difficulties (82.1%), fear/anxiety (55.2%), pain (23.9%), insomnia (14.9%), and choking (14.93%). Oxygen was given to 52.6% of patients, and pain medications were given to 74%. CONCLUSION: These data suggest that palliative care at the end of life was relatively well managed for most patients with ALS who participated in this study; nevertheless, several opportunities for improvement were identified.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".