Palliative Care Needs in Patients With Early-Onset Alzheimer’s Disease: A Cross-Sectional Report
Bibliographic record
Abstract
Background Early-onset Alzheimer’s disease is a rapidly progressing condition that severely disrupts quality of life. Early palliative care incorporation helps identify patients’ needs and facilitates family decision-making and advanced care planning for later stages. Aim The study aimed to assess palliative care needs, level of suffering, and most frequent symptoms in Early-onset Alzheimer’s patients. Methods We present a preliminary observational analysis as part of a larger, 18-month longitudinal study of patients with early-onset Alzheimer’s disease. Patients with the PSEN1-E280 A variant of Early-onset Alzheimer’s disease attending the Antioquia Neuroscience Group at the University of Antioquia in Medellín, Colombia, participated. Data were collected using NECPAL, Edmonton Symptom Assessment System Revised, Global Deterioration Scale/Functional Assessment Staging (GDS/FAST), Pain Assessment in Advanced Dementia (PAINAD), and a numeric rating scale. Regarding data analyses from visit 1 (V1), variables were described according to their nature. A Poisson regression was performed, and prevalence ratios, 95% confidence intervals, and P values were obtained. Statistical significance was defined with an alpha value of 5%. Results Thirty-six patients participated in V1. Median age was 53. Most of them were women and lived in urban areas. The prevalence of palliative care needs was 22.22%. Poisson regression showed an association between clinician-perceived need for palliative care and dysphagia, pressure ulcers, asthenia, insomnia, functional decline, resource utilization, positive surprise question in NECPAL instrument, and scores of the PAINAD and GDS/FAST scales. Conclusion Patients with Early-onset Alzheimer’s have palliative care needs associated with symptoms related to disease progression, prognosis, resource utilization, and pain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".