Editorial: Personalised multimodal prehabilitation in cancer
Bibliographic record
Abstract
Editorial on the Research Topic Personalised multimodal prehabilitation in cancerMultimodality prehabilitation is a complex intervention that can enhance fitness, nutrition, and psychological resilience, with emerging evidence showing an improvement in perioperative and oncological outcomes (1).Personalised prehabilitation also has the potential to meet the widely adopted triple aim of health care: improving individuals' experience of care, improving population health, and providing value for money to the taxpayer (2).The contemporary prehabilitation model has adopted a multimodal approach, which attempts to address complex needs in patients having complex treatment pathways.Multimodal prehabilitation incorporates intervention components specifically selected for their potential synergistic effects on health outcomes.Prehabilitation enables people with cancer prepare for treatment through promoting healthy behaviours and through needs-based prescribing of exercise, nutrition, and psychological interventions, aiming to empower patients to maximise resilience to treatment and improve long-term health outcomes (3).In this Research Topic entitled 'Personalised Multimodal Prehabilitation in Cancer' a collection of articles demonstrate how prehabilitation is now regarded as an integral part of a continuum spanning from cancer diagnosis to rehabilitation.Multimodal prehabilitation interventions are often comprised of two or more of the following: i) aerobic and resistance training to attenuate cardiorespiratory and musculoskeletal deconditioning, ii) dietary interventions to counteract disease and/or treatment-related malnutrition, support anabolism and the metabolic cost of exercise; iii) psychological interventions to reduce stress, anxiety and associated morbidity; iv) the cessation of adverse health behaviours; and v) behavioural modification to support intervention initiation and adherence in the perioperative setting, whilst establishing selfmanagement skills for long-term health behaviour change.In the perioperative setting, Frontiers in Oncology frontiersin.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".