Integrating palliative care into care of patients with kidney cancer and melanoma.
Bibliographic record
Abstract
56 Background: Early palliative care (PC) has been shown to improve quality of life (QOL), enhance quality end-of-life (EOL) care, and reduce costs, yet many cancer centers lack resources to provide outpatient palliative care services. Data is needed to identify the optimal timing and most effective and efficient model for integrating PC into the care of cancer patients. In order to understand what components of PC have the greatest impact on patients, we examined the acceptability of early PC among patients with kidney cancer (RCC) and melanoma (M) and describe the content of PC visits. Methods: In July 2013, the outpatient PC team at Beth Israel Deaconess Medical Center, including physician (MD), social worker (SW) and chaplain (chap) were invited to see patients within a clinic that specializes in seeing patients with advanced RCC and M. All patients completed data on symptom burden using the Edmonton Symptom Assessment Scale, measures of QOL and degree of psycho-social and spiritual support. Referral to PC was based on these metrics and physician discretion. Results: Of the 21 patients seen by PC, 57% had RCC; 76% male; 86% White; 57% married. Mean age was 62 (range: 36-87). At the 1st PC visit: All had locally adv/metastatic disease; 48% were being treated with curative intent; 29% had not yet started treatment; 57% rated pain 0 on 0-10 scale (range: 0-9). First PC visit content: 76% psychosocial support (including building rapport); 67% symptom management; 33% included advance care planning (ACP) and decision-making support. Ninety-one percent were seen >1 by PC; 19% seen by SW or chap; median PC visits: 3. Eighty-one percent completed health care proxy. Additional data on patient outcomes will be presented. Conclusions: Nearly half of patients seen by PC were being treated with curative intent, suggesting acceptability of early PC integration in patients with RCC and M. Symptom management and psychosocial support dominated the early PC visits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".