Prognostic significance of novel muscle quality index utilization in hospitalized adults with cancer: A secondary analysis
Bibliographic record
Abstract
Abstract Background This study aimed to investigate and propose novel approaches to calculate muscle quality index (MQI) using muscle mass derived from single‐frequency bioelectrical impedance analysis (SF‐BIA) and calf circumference in both unadjusted and body mass index (BMI)–adjusted forms. In addition, we examined their prognostic significance in patients with cancer. Methods A secondary analysis was conducted on a prospective cohort study of patients with cancer. Handgrip strength was measured. SF‐BIA was conducted to estimate appendicular lean soft tissue (ALST, in kilograms). MQI was calculated using three approaches: (1) the ratio of handgrip strength to ALST (MQISF‐BIA), (2) the ratio of handgrip strength to calf circumference (MQIcalf circumference), and (3) the ratio of handgrip strength to BMI‐adjusted calf circumference (MQIadj. calf circumference). Maximally selected log‐rank was calculated to estimate their cutoff values to predict survival. Results Two hundred eighty‐four patients were included (51.1% men; median age, 61 years). Solid tumors were the most frequent (89.8%). All approaches to MQI (MQISF‐BIA, MQIcalf circumference, and MQIadj. calf circumference) were independent predictors of 6‐month mortality. The found cutoffs were (1) MQISF‐BIA (<1.52 for men, <0.63 for women), (2) MQIcalf circumference (<0.74 for men, <0.24 for women), and (3) MQIadj. calf circumference (<0.75 for men, <0.25 for women). Conclusion This study introduces MQISF‐BIA, MQIcalf circumference, and MQIadj. calf circumference as future potential surrogate methods for computing MQI in clinical practice when other robust procedures are unavailable, pending further validation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".