A longitudinal study of palliative care
Bibliographic record
Abstract
BACKGROUND: The current article evaluated the course of patient-assessed symptomatology in specialized palliative care and tested for bias due to patient attrition in measures of initial symptomatology and treatment outcome. METHODS: Over 2 years, 267 consecutive, eligible patients were referred to a department of palliative care. Upon arrival, 201 patients consented to participate in a questionnaire-based evaluation of quality of life (QOL). Of these, 175 patients participated, and 142, 119, and 95 participated in the study at 1, 2, and 3 weeks, respectively. Weekly, participants completed the self-assessment questionnaires European Organization for Research and Treatment of Cancer QLQ-C30, Edmonton Symptom Assessment System, Hospital Anxiety and Depression Scale, and Multidimensional Fatigue Inventory. Physicians used the Mini Mental State Examination to evaluate cognitive function. Changes from the initial symptom scores for each week were calculated. Initial scoring and change after 1 week were tested for association with completion level, i.e., whether the patient completed questions at 1, 2, 3, or 4 time points. RESULTS: High initial symptom intensity and significant improvements over time were observed for pain, lack of appetite, nausea/vomiting, sleeplessness, constipation, and overall QOL/well-being. For some symptoms, initial scores were significantly higher in patients who dropped out, but the changes over the first week were not significantly different between completion levels for any symptom. CONCLUSIONS: Improvement in symptom intensity was identified. Dropout was associated with higher initial symptomatology but not with poorer outcome of palliative treatment.
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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 | 0.005 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".