Symptom Burden Poorly Responsive to Palliative Care Intervention and Karnofsky Predict Survival in an Acute Palliative Care Unit
Bibliographic record
Abstract
Background/Objective: Survival prediction in the advanced cancer care setting plays a vital role in treatment planning and patients’ arrangements. The aim of this study was to examine the association of the global Edmonton Symptom Assessment System (GESAS) and Karnofsky scale (KPS) with overall survival (OS) in patients with advanced cancers admitted to an acute palliative care unit (APCU). The second aim was to assess if GESAS changes after comprehensive palliative treatment could influence OS. Methods: This is a prospective planned sub-analysis of advanced cancer patients. A consecutive sample of 521 patients admitted to an APCU. Patients with available survival in follow-up phone calls, having complete ESAS, and discharged alive were selected. KPS and GESAS were measured at admission and after seven days of individual comprehensive palliative care. Results: Two hundred forty-three of 521 screened patients were assessed according to inclusion criteria. The mean age was 67.1 years (SD 11.5), and 121 patients were male. The mean KPS was 43.5 (SD 9.3). The mean OS was 74.6 (SD 136.2) days. Significant changes in GESAS were observed after one week. Univariate linear regression analysis showed that KPS and GESAS at T0 and at T7 were correlated with OS (p < 0.0005; p = 0.020; p < 0.0005, respectively). At multivariate analysis, OS was correlated with KPS and GESAS at discharge (B = 3.349, 95% CI = 1.560–5.137; B = −2.430, 95% CI = −3.831–−1.029). Discussion: KPS and poor response to intensive treatment, maintaining high GESAS scores, can be considered predictive factors of shorter OS. Further studies should confirm whether a specialized intervention in other settings can improve OS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".