Supporting the Needs of Adolescents and Young Adults: Integrated Palliative Care and Psychiatry Clinic for Adolescents and Young Adults with Cancer
Bibliographic record
Abstract
Clinical guidelines aimed at cancer care for adolescents and young adults (AYAs) encourage early integration of palliative care, yet there are scarce data to support these recommendations. We conducted a retrospective chart review of AYA patients, aged 15 to 39 years, who were referred to the Integrated AYA Palliative Care and Psychiatry Clinic (IAPCPC) at the Princess Margaret Cancer Centre between May 2017 and November 2019 (n = 69). Demographic data, symptom prevalence, change in symptom scores between baseline consultation and first follow-up, and intensity of end-of-life care were collected from the patients’ medical charts, analyzed, and reported. Of the 69 patients, 59% were female, and sarcoma was the most common cancer. A majority of patients had at least one symptom scored as moderate to severe; tiredness, pain, and sleep problems were the highest scored symptoms. More than one-third used medical cannabis to manage their symptoms. Symptom scores improved in 61% after the first clinic visit. Out of the 69 patients, 50 (72.5%) had died by October 2020, with a median time between the initial clinic referral and death of 5 months (range 1–32). Three patients (6%) received chemotherapy, and eight (16%) were admitted to an intensive care unit during the last month of life. In conclusion, AYAs with advanced cancer have a high burden of palliative and psychosocial symptoms. Creating a specialized AYA palliative care clinic integrated with psychiatry showed promising results in improving symptom scores and end-of-life planning.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".