The Role of a Psychiatrist (Rehabilitation Physician) in the Palliative Care Team
Bibliographic record
Abstract
Objective: Although palliative care consultation teams (PCTs) have become an integral part of all regional cancer care hospitals, cancer rehabilitation is still in the developing stage in Japan. At our hospital, a physiatrist, or a rehabilitation physician, joined the PCT as a member, and we have been achieving good results. The purpose of this study is to clarify the role of a physiatrist as a member of the PCT by investigating the current status of the PCT at our hospital.Method: We retrospectively analyzed the records of 144 patients receiving rehabilitation out of 345 patients who had started receiving PCT services from April 2012 to March 2013 at Keio University Hospital.Results: Among the 144 patients, 67 patients (46.5%) had already received rehabilitation before starting PCT services, and the remaining 77 patients (53.5%) started receiving rehabilitation based on the recommendation by the physiatrist. In the later group, Dietz Cancer Stage Classifications were: preventative, 6.5%; restorative, 40.0%; supportive, 45.5%; and palliative, 9.1%. ECOG-PS were: PS0, 0.0%; PS1, 23.4%; PS2, 29.9%; PS3, 28.6%; and PS4, 18.2%. The main purposes of rehabilitation were: 1) fracture prevention and pain relief of limbs with bone lesions, 2) social support (e.g. measures for the reduction of nursing requirement, assistance in finding social aids, counseling for family members), 3) psychological support, 4) treatment of lymphedema, 5) management of breathlessness, 6) and approaches to swallowing deficits.Conclusions: Half of the cancer patients started rehabilitation after the intervention by the PCT. Many patients receiving PCT services also needed rehabilitation services and the physiatrist had an important role of evaluating the rehabilitation needs. Physiatrists can act as important members of PCTs to maximize QOL while addressing the physical, psychological, social and spiritual needs of both patients and their families.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".