Screening instruments for early identification of unmet palliative care needs: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The early detection of individuals who require palliative care is essential for the timely initiation of palliative care services. This systematic review and meta-analysis aimed to (1) Identify the screening instruments used by health professionals to promote early identification of patients who may benefit from palliative care; and (2) Assess the psychometric properties and clinical performance of the instruments. METHODS: A comprehensive literature search was conducted in PubMed, Embase, CINAHL, Scopus, CNKI and Wanfang from inception to May 2023. We used the COnsensus-based Standards for the Selection of Health Measurement INstruments to assess the methodological quality of the development process for the instruments. The clinical performance of the instruments was assessed by narrative summary or meta-analysis. Subgroup analyses were conducted where necessary. The quality of included studies was assessed using the Newcastle-Ottawa Scale and the Cochrane Collaboration's risk of bias assessment tool. RESULTS: We included 31 studies that involved seven instruments. Thirteen studies reported the development and validation process of these instruments and 18 studies related to assessment of clinical performance of these instruments. The content validity of the instruments was doubtful or inadequate because of very low to moderate quality evidence. The pooled sensitivity (Se) ranged from 60.0% to 73.8%, with high heterogeneity (I2 of 88.15% to 99.36%). The pooled specificity (Sp) ranges from 70.4% to 90.2%, with high heterogeneity (I2 of 96.81% to 99.94%). The Supportive and Palliative Care Indicators Tool (SPICT) had better performance in hospitals than in general practice settings (Se=79.8% vs 45.3%, p=0.004; Sp=59.1% vs 97.0%, p=0.000). CONCLUSION: The clinical performance of existing instruments in identifying patients with palliative care needs early ranged from poor to reasonable. The SPICT is used most commonly, has better clinical performance than other instruments but performs better in hospital settings than in general practice settings.
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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 | 0.037 | 0.088 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.052 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".