Integrating Supportive and Palliative Care in the Trajectory of Cancer: Establishing Goals and Models of Care
Bibliographic record
Abstract
Tom, a 50-year-old man with metastatic pancreatic cancer, was referred by his phase I physician to our supportive care center for symptom management. He was initially diagnosed with pancreatic cancer involving the liver approximately 6 months before this visit. He developed progressive disease despite three lines of systemic therapy, including four cycles of gemcitabine and cisplatin, three cycles of gemcitabine and vorinostat under phase I, and two cycles of fluorouracil, leucovorin, and oxaliplatin 6. His clinical course was complicated by recurrent venous thromboembolic events. Tom arrived at the supportive care clinic in a wheelchair and accompanied by his wife. He complained of right upper quadrant pain, particularly with inspiration, despite taking morphine continuous release 30 mg twice a day. He had also experienced severe fatigue, weakness, constipation, decreased appetite, and weight loss of 25 kg during the previous 5 months. Eastern Cooperative Oncology Group performance status was 4. Edmonton Symptom Assessment Scale (0 no symptom, 10 worst possible) revealed pain 8, fatigue 4, nausea 0, depression 0, anxiety 0, drowsiness 0, appetite 10, well-being 5, dyspnea 5, and sleep 5. On examination, he looked thin, was tachycardic and tachypneic, and had significant tenderness over the right upper quadrant. He was also experiencing severe weakness and had to use both hands to lift his legs to move around in bed. He lived with his wife and one son at home, but he had difficulty coping with the worsening symptoms. We adjusted his pain medications and laxatives, gave him a dose of methylnaltrexone in the clinic, recommended a home-safety evaluation, and provided supportive/expressive counseling. He expressed the desire not to receive any more cancer treatments, and we discussed the transition to hospice care so he could get more support at home. At the end of the visit, Tom said, “Oh, I wish I had seen you sooner. Why wasn’t I referred here earlier?” Tom went home with hospice care and died 10 days later.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".