The TEAM Approach to Improving Oncology Outcomes by Incorporating Palliative Care in Practice
Bibliographic record
Abstract
Palliative care (PC) concurrent with usual oncology care is now the standard of care that is recommended for any patient with advanced cancer to begin within 8 weeks of diagnosis on the basis of evidence-driven national clinical practice guidelines; however, there are not enough interdisciplinary palliative care teams to provide such care. How and what can an oncology office incorporate into usual care, borrowing the tools used in PC randomized clinical trials (RCTs), to improve care for patients and their caregivers? We reviewed the multiple RCTs for common practical elements and identified methods and techniques that oncologists can use to deliver some parts of concurrent interdisciplinary PC. We recommend the standardized assessment of patient-reported outcomes, including the evaluation of symptoms with such tools as the Edmonton or Memorial Symptom Assessment Scales, spirituality with the FICA Spiritual History Tool or similar questions, and psychosocial distress with the Distress Thermometer. All patients should be assessed for how they prefer to receive information, their current understanding of their situation, and if they have considered some advance care planning. Approximately 1 hour of additional time with the patient is required each month. If the oncologist does not have established ties with spiritual care and social work, he or she should establish these relationships for counseling as required. Caregivers should be asked about coping and support needs. Oncologists can adapt PC techniques to achieve results that are similar to those in the RCTs of PC plus usual care compared with usual care alone. This is comparable to using data from RCTs of trastuzamab or placebo, adopting what was used in the RCTs without modification or dilution.
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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 | 0.052 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".