Bibliographic record
Abstract
Cancer is a systemic disease that can affect nearly every organ in the body, resulting in a progressive loss of organ function. That loss of function may be initially slow, having minimal effect, or it may be rapid, resulting in more dramatic changes.The usual medical management of patients with cancer has focused more specifically on the administration of cytotoxic treatments. These treatments can potentially eradicate or minimize the tumour, but they may also have toxic side effects that in turn can also affect the patient.Cancer rehabilitation is a process that assists the individual with a cancer diagnosis to obtain optimal physical, social, psychological, and vocational functioning within the limits created by the disease and its treatment. The McGill Cancer Nutrition and Rehabilitation (CNR) program developed as a result of the ever-increasing demand for a focus on addressing individual cancer patients and their needs, as well as on achieving optimal tumour-related outcomes. Using an interdisciplinary approach, the CNR's global objective is to empower individuals who are experiencing loss of function, fatigue, malnutrition, psychological distress, and other symptoms as a result of cancer or its treatment to improve their own quality of life. All team members-experts in their respective fields-assess all patients. At a subsequent team discussion and planning meeting, a specific 8-week program is designed for each patient. The hoped-for outcome for the CNR program is primarily to empower patients to "take control" or to enable them to improve their own quality of life. This article reviews the philosophy of the CNR's approach and the roles played by the various members of the team.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".