PROMs from hospital to hospice.
Bibliographic record
Abstract
209 Background: Symptom burden in advanced cancer patients (pts) influences quality of life (QoL) and should be leading in advanced care planning for both hospital and hospice populations. The objective is to describe the course of symptomburden in those patients over time. Methods: Symptoms were biweekly assessed with the self-assessment tool Edmonton Symptom Assessment System (ESAS; Dutch translation) in a prospective longitudinal study of hospitalized patients and patients admitted to a hospice. Prevalence and intensity data were entered in a web-based database by distinguishing patients into 3 groups regarding the aim of palliative care: tumor palliation, symptom palliation and terminal care. Results: 808 pts included, mean age 65 (18-93), 57% female; 224 (30%) pts admitted in the hospice-setting, 584 (70%) in a medical oncology ward. The care aimed 36 % tumor palliation, 59% symptom management, 4% terminal care. Most prevalent symptoms were similar in hospital and hospice: fatigue, anorexia, dry mouth, pain, constipation. The highest clinical relevance (score > 4 on NRS 0-10; 0 = no symptom, 10 = worst symptom) was found in hospital for fatigue (56%), anorexia (54%), constipation (48%), dry mouth (44%), pain (33%). In hospice patients fatigue (88%), dry mouth (66%), anorexia (63%), pain (4%), constipation (40%). In both care settings same national guidelines for symptom management are used as bottom line for interventions. 48% hospital patients scored > 4 for overall wellbeing (0= very good, 10 = very bad) vs 68% in hospice. In the hospice cohort pts are discriminated by prognosis, symptomburden, QoL: > 3 months survival, 2 – 12 weeks, < 2 weeks. Analysis is ongoing, data will be presented at the conference. Conclusions: The course of symptom intensity in advanced cancer populations in hospital and hospice setting is quite similar. A combination of general, specialized and expert palliative care competencies are needed during the cancer continuum. Prospective monitoring of patients during the continuum is a methodological and practical challenge.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.116 | 0.023 |
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".