Impact of integrated palliative care model on end-of-life (EOL) quality metrics for patients with kidney cancer (RCC) and melanoma (M).
Bibliographic record
Abstract
137 Background: Early palliative care (PC) improves quality of life (QOL) and enhances end-of-life (EOL) care, but the optimal timing and most effective model for integrating PC into oncologic care is uncertain. To understand the impact of an integrated model with PC providers embedded with oncologists vs. usual care (UC) with referral at the discretion of the same oncologists, we examined the timing and delivery of PC and Quality Oncology Practice Initiative (QPOI) EOL metrics among patients with RCC and M in a single clinic. We hypothesized that integrated PC would result in more referrals, earlier contact with PC and better QOPI EOL metrics compared with UC. Methods: In a retrospective cohort study of patients with RCC and M in the Beth Israel Deaconess Biologics Clinic who expired between 10/1/12 and 12/31/14, we compared patients seen 2 days/week, when referral to PC was discretionary, with a third day when PC providers shared the clinic for real-time consultations. Patients were identified as meeting PC eligibility if they had recurrent, metastatic disease and were on active treatment or had a symptom severity of 7+ on Edmonton Symptom Assessment Scale (ESAS). Two oncologists saw all patients, regardless of day. Results: Seventy-six patients expired, 19 in the Integrated PC model and 57 with UC. Patients were similar with respect to diagnosis and demographics except for smoking. The integrated model substantially improved timing and location of PC. In the integrated PC model, 85% were seen by PC compared with 45% in UC (P = 0.002). All patients in the integrated model began PC as an outpatient compared with 36% in UC (P < 0.001). The mean number of days from first PC contact to death was 28 (SD = 54) for UC and 118 (SD = 120) with integrated PC (P < 0.001). The location of death did not differ significantly between models, occurring outside the hospital with hospice among 71% of patients in the integrated model and 53% in UC (P = 0.25). Results were similar in relative risk models adjusted for smoking. Conclusions: A practice model that integrated PC with oncologic care was associated with more PC referrals, earlier contact, and a nonsignificant trend toward fewer deaths in hospital and ICU.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".