Patient-Rated Acceptability of Automatic Palliative Care Referral: A Prospective Cohort Study
Bibliographic record
Abstract
CONTEXT: Timely palliative care can alleviate distress after diagnosis of an incurable cancer. However, late referrals to palliative care continue, reflecting various provider and patient barriers. OBJECTIVES: To determine patient/caregiver-reported acceptability of a phone call offering a supportive and palliative care (SPC) consultation without requiring oncologist referral. METHODS: Two SPC nurses screened out-patient clinic lists at a tertiary cancer center weekly and called all eligible patients to offer an SPC consultation. Eligibility: >18 years, newly diagnosed/suspected stage IV nonsmall cell lung cancer, and completed first oncologist visit. Patients/caregivers were surveyed about the acceptability of the phone call offering SPC consultation, using Sekhon's Framework of Acceptability domains. RESULTS: Among 113 patients screened, 81 patients/caregivers were contacted and offered an SPC consultation; 72% accepted the consultation. Of 48 patients/caregivers surveyed, 94% rated overall acceptability of the call somewhat/completely acceptable; 6% rated it neither acceptable nor unacceptable. Within specific acceptability domains, 95% were comfortable receiving the call; 92% understood why they received the call; 87% found the call valuable; 70% found the call helpful; 66% learned about SPC from the call; no one expressed concern that the SPC nurse had access to their contact/health information; 97% thought the call required little physical/emotional effort and were confident in their ability to participate (i.e., to ask questions/make decisions). CONCLUSION: These unsolicited phone calls offering SPC consultation were highly acceptable to patients/caregivers, and most agreed to the consultation. Implementing routine calls offering SPC consultation may be a timely alternative to awaiting conventional oncologist referral.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".