High Symptom Burden and Low Functional Status in the Setting of Multimorbidity
Bibliographic record
Abstract
OBJECTIVES: To enhance understanding of the relationship between multimorbidity, symptom burden, and functional status in individuals with life-limiting illness. DESIGN: Secondary analysis of baseline data from a randomized clinical trial conducted in the Palliative Care Research Cooperative Group. Group differences were tested using a t-test; multivariate regression analysis was used to determine the effect of multiple variables on functional status and symptom burden. SETTING: Fifteen Palliative Care Research Cooperation sites. PARTICIPANTS: Adults who participated in a parent statin-discontinuation clinical trial were included in the analysis (N = 381). Inclusion criteria were diagnosis of a life-limiting illness, statin use for 3 months or longer, life expectancy longer than 1 month, and declining functional status. MEASUREMENTS: Cancer diagnosis (solid organ and hematologic malignancies), multimorbidity (Charlson Comorbidity Index (CCI) score), symptom burden (Edmonton Symptom Assessment Scale (ESAS) score, number of symptoms with ESAS severity score >4), functional status (Australia-modified Karnofsky Performance Scale (AKPS)). RESULTS: Fifty-one percent had a primary diagnosis of cancer; mean age 74.1 ± 11.6. Participants had multiple comorbid illnesses (CCI score 4.9 ± 2.8), multiple symptoms (ESAS score 27.2 ± 15.9), and poor functional status (AKPS = 53 ± 13). In univariate and multivariate analyses, multimorbidity was associated with greater symptom burden (4.2 vs 3.1 moderate or severe symptoms (t = -3.2, P = .002), 12% vs 6% with severe symptoms (t = -3.7, P < .001)), but cancer diagnosis was not. In univariate and multivariate analyses, higher symptom burden was associated with poorer functional status (F = 11.6, P < .001), but multimorbidity was not. CONCLUSION: Symptoms cannot be attributed solely to a diagnosis of cancer. The association between symptom burden and functional status underscores the importance of clinical attention to symptoms in individuals with multimorbidity.
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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 | 0.006 |
| 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".