Effectiveness of Specialized Palliative Care
Bibliographic record
Abstract
CONTEXT: Specialized palliative care teams are increasingly providing care for the terminally ill. However, the impact of such teams on quality of life, satisfaction with care, and economic cost has not been examined systematically using detailed criteria for study quality. OBJECTIVE: To systematically review the evidence for effectiveness of specialized palliative care. DATA SOURCES: We performed a keyword search of the following databases from their inception to January 2008: MEDLINE, Ovid Healthstar, CINAHL, EMBASE, and the Cochrane Central Register of Controlled Trials. STUDY SELECTION: We included all randomized controlled trials in which specialized palliative care was the intervention and for which outcomes included quality of life, satisfaction with care, or economic cost. DATA EXTRACTION: Data on population, intervention, outcome, methods, and methodological quality were extracted by 2 investigators using standardized criteria. RESULTS: Of 396 reports of randomized controlled trials, 22 met our inclusion criteria. There was most consistent evidence for effectiveness of specialized palliative care in improvement of family satisfaction with care (7 of 10 studies favored the intervention). Only 4 of 13 studies assessing quality of life and 1 of 14 assessing symptoms showed a significant benefit of the intervention; however, most studies lacked statistical power to report conclusive results, and quality-of-life measures were not specific for terminally ill patients. There was evidence of significant cost savings of specialized palliative care in only 1 of the 7 studies that assessed this outcome. Methodological limitations were identified in all trials, including contamination of the control group, failure to account for clustering in cluster randomization studies, and substantial problems with recruitment, attrition, and adherence. CONCLUSIONS: The evidence for benefit from specialized palliative care is sparse and limited by methodological shortcomings. Carefully planned trials, using a standardized palliative care intervention and measures constructed specifically for this population, are needed.
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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 | 0.024 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".