Symptom distress and patterns of palliative-care referral among oncology outpatients.
Bibliographic record
Abstract
185 Background: The early implementation of palliative care services is recognized as an important aspect of oncologic care. However, rates of referral to palliative care services among oncology patients are still low, and the decision to refer is frequently at the discretion of the treating oncologist or by patient request. We sought to better identify the patterns of referral to outpatient palliative care, as well as patient symptom burden in an effort to target early and high-yield palliative care interventions. Methods: We conducted a cross-sectional survey among outpatients presenting to a Hematology/Oncology practice at a tertiary care hospital. Patients presenting to the clinic were asked to complete an Edmonton Symptom Assessment Scale (ESAS) survey at time of registration. Chart review was completed to identify basic demographic information, timing and extent of cancer diagnosis, basic medical and psychiatric comorbidities, and existing referral to palliative care services. Results: Between November 15, 2014 and December 24, 2014, a total of 146 complete surveys were collected from oncology outpatients. The most common malignancies were hematologic (40.4%), lung (24.6%), breast (8.2%), gastrointestinal (6.8%) and genitourinary (6.8%); 30.1% had metastatic disease at the time of the visit. A total of 13 patients (8.9%) were receiving outpatient palliative care services. As compared to patients not receiving palliative care services, those who were reported higher overall symptom distress scores (26.3 vs. 12.7, p = 0.013) and pain scores (3.5 vs. 1.6, p = 0.03). Patients receiving palliative care services also had fewer years since diagnosis (2.8 years vs. 4.5 years, p = 0.028), and a non-significant trend toward higher rates of metastatic disease (72.7% vs. 47.3%, p = 0.059). Conclusions: Overall, low rates of referrals to palliative care were found among oncology outpatients. In addition, this study suggests oncology patients are referred to palliative care at later stages of disease, when they are already experiencing significant symptom burden. Future research will determine which patients will benefit from earlier referrals to palliative care before symptoms become more advanced.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".