Using minimal clinically important differences to measure long-term transitions of osteosarcopenia: The New Mexico Aging Process Study
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: By having a better understanding of transitions in osteosarcopenia, interventions to reduce morbidity and mortality can be better targeted. The aim of this study was to show the rationale and method of using minimal clinically important differences (MCID's) to classify transitions, and the effects of demographic variables on transitions in a 9-year follow-up data from the New Mexico Aging Process Study (NMAPS). METHODS: Transitions were identified in four aspects of osteosarcopenia: bone mineral density (BMD), appendicular skeletal muscle mass/body mass index ratio (ASM/BMI), grip strength and gait speed. Transitions were identified using a MCID score. As there is currently no available MCID for BMD and ASM/BMI, those were determined using a distribution-based and an anchor-based method. Total transitions were calculated for all four measures of osteosarcopenia in all transition categories (maintaining a health status, beneficial transition, harmful transitions). Poisson regression was used to test for effects of demographic variables, including age, sex, physical activity, medication, and health status, on transitions. RESULTS: Over the 9-year follow-up, a total of 2163 MCID-derived BMD transitions were reported, 1689 ASM/BMI transitions, 2339 grip strength transitions, and 2151 gait speed transitions. Additionally, some MCID-derived transition categories were associated with sex, age, and health status. CONCLUSION: Use of MCID-derived transitions reflected the fluctuation and the dynamic nature of health in older adults. Future research should focus on transitions of modifiable markers in osteosarcopenia to design intervention trials.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".