Referral Criteria for Outpatient Palliative Cancer Care: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Outpatient palliative care clinics facilitate early referral and are associated with improved outcomes in cancer patients. However, appropriate candidates for outpatient palliative care referral and optimal timing remain unclear. We conducted a systematic review of the literature to identify criteria that are considered when an outpatient palliative cancer care referral is initiated. METHODS: We searched Ovid MEDLINE (1948-2013 citations) and Ovid Embase (1947-2015 citations) for articles related to outpatient palliative cancer care. Two researchers independently reviewed each citation for inclusion and extracted the referral criteria. The interrater agreement was high (κ = 0.96). RESULTS: Of the 186 publications in our initial search, 21 were included in the final sample. We identified 20 unique referral criteria. Among these, 6 were recurrent themes, which included physical symptoms (n = 13 [62%]), cancer trajectory (n = 13 [62%]), prognosis (n = 7 [33%]), performance status (n = 7 [33%]), psychosocial distress (n = 6 [29%]), and end-of-life care planning (n = 5 [24%]). We found significant variations among the articles regarding the definition of advanced cancer and the assessment tools for symptom/distress screening. The Edmonton Symptom Assessment Scale (n = 7 [33%]) and the distress thermometer (n = 2 [10%]) were used most often. Furthermore, there was a lack of consensus in the cutoffs in symptom assessment tools and timing for outpatient palliative care referral. CONCLUSION: This systematic review identified 20 criteria including 6 recurrent themes for outpatient cancer palliative care referral. It highlights the significant heterogeneity regarding the timing and process for referral and the need for further research to develop standardized referral criteria. IMPLICATIONS FOR PRACTICE: Outpatient palliative care clinics improve patient outcomes; however, it remains unclear who is appropriate for referral and what is the optimal timing. A better understanding of the referral criteria would help (a) referring clinicians to identify appropriate patients for palliative care interventions, (b) administrators to assess their programs with set benchmarks for quality improvement, (c) researchers to standardize inclusion criteria, and (d) policymakers to develop clinical care pathways and allocate appropriate resources. This systematic review identified 20 criteria including 6 recurrent themes for outpatient palliative cancer care referral. It represents the first step toward developing standardized referral criteria.
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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 | 0.021 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".