Developing pathways for cancer rehabilitation in tertiary oncology centers: Initial observations at the McGill University Health Centre.
Bibliographic record
Abstract
17 Background: No definitive rehabilitation pathways exist for cancer patients. To address this gap, the Cancer Rehabilitation interdisciplinary team at the McGill University Health Centre has developed three program paths (e.g., Restorative, Supportive, and Cachexia) to meet the various specialized and personalized needs of cancer patients. Methods: A consecutive cohort of patients referred to the Cancer Rehabilitation Clinic between January 1st and June 30th, 2014 was considered. We examined the following baseline characteristics: handgrip strength (HGS), the abridged Patient Generated-Subjective Global Assessment (aPG-SGA) and Edmonton Symptom Assessment System (ESAS) self-reported questionnaires. Results: Of the 54 patients evaluated (57.4% male), 20 (mean age: 47.4 yrs), 8 (59.9 yrs) and 26 (64.6 yrs) were assigned to the restorative, supportive and cachexia streams, respectively. The most common cancer diagnoses were gastrointestinal (15%), gynecological (13%), breast (12%) and lung (12%). Table 1 contains baseline aPG-SGA, ESAS and HGS scores. Conclusions: Our preliminary data confirm clinically significant differences in muscle strength across the 3 streams for both males and females, as well as significant differences in nutritional, appetite and well-being scores between the patients in the restorative and cachexia pathways. Our data confirm the need of personalized and targeted interventions to achieve or maintain optimal performance and quality of life in cancer survivors with different disease and treatment characteristics. [Table: see text]
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".