The Interdisciplinary Rehabilitation Care Team and the Role of Physical Therapy in Survivor Exercise
Bibliographic record
Abstract
BACKGROUND: Rehabilitation professionals offer expertise in functional assessment, treatment of impairments and functional limitations, and disability prevention. To optimize recovery, and often prior to participating in community-based exercise programming, survivors may need rehabilitation services from a range of healthcare professionals, including physiatrists, nurses, nutritionists, psychologists, and speech, occupational, and physical therapists. OBJECTIVES: Survivors with physical impairments and functional limitations may benefit from interdisciplinary rehabilitation and physical therapy, including tailored therapeutic exercise interventions. METHODS: A literature review was conducted using the key words cancer survivor, cancer rehabilitation, impairment, fatigue, lymphedema, chemotherapy-induced peripheral neuropathy, and exercise. MEDLINE®, EMBASE, Cochrane Database of Systematic Reviews, and CINAHL® databases were searched. FINDINGS: Nurses play a critical role in identifying survivors whose function or fitness is compromised to the point where participation in community-based exercise programming would be inappropriate or unsafe. The interdisciplinary rehabilitation care team can help facilitate the survivor's transition to community-based exercise programming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".