Effects of multimodal prehabilitation on muscle size, myosteatosis, and dietary intake of surgical patients with lung cancer — a randomized feasibility study
Bibliographic record
Abstract
Many patients with lung cancer undergo surgery, which can increase the risk for muscle loss, leading to worsened outcomes. A multimodal prehabilitation intervention integrating dietary and muscle assessment may help clinicians better understand changes in these outcomes. This pilot assessed feasibility of multimodal prehabilitation in early-stage surgical lung cancer patients and explored relationships between body composition, muscle characteristics and dietary intake, as well as muscle changes due to prehabilitation. Patients were randomized to 1 of 2 groups: multimodal prehabilitation including nutritional supplements (fish oil with vitamin D3 + whey protein with leucine), exercise and relaxation, or standard of care. Physical function, dietary intake and muscle were evaluated at 0 and 4 weeks pre-operatively. Of 87 patients assessed for eligibility, 34 (39%) were randomized and 3 (9%) were lost to follow-up. Median age was 69 years and baseline protein intake was 1.0 g/kg/day. Adherence to exercise (86%) and supplements was high (93%); 3 patients (16%) reported side effects. Supplements significantly increased protein, omega-3 fatty acid, leucine and vitamin D intake. There were no significant changes in muscle characteristics. Multimodal prehabilitation with dietary and muscle analyses proved to be feasible. An adequately powered randomized controlled trial is warranted. ClinicalTrials.gov registration no: NCT04610606. Novelty: Multimodal prehabilitation incorporating dietary assessment and muscle analysis is feasible for early-stage surgical lung cancer patients. An adequately powered randomized controlled trial is warranted to further explore functional and post-operative outcomes.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".