DEFINING OPPORTUNITIES FOR NATIONAL SURVEY DATA TO IDENTIFY RISKS FOR FRAILTY AND MALNUTRITION
Bibliographic record
Abstract
The rapidly growing older adult population will continue to place significant demands on the US healthcare system. Screening for health conditions to treat early, maintain functionality, and support healthy aging is critical. Frailty is now one of the top 10 geriatric concerns and is clinically tied to increased risk of negative health outcomes; falls, hospitalization, disability, and death. Malnutrition has been associated with adverse health outcomes and conditions, including frailty. However, these conditions are not singled out for attention in national health objectives and there are currently no identified key health indicators for tracking these conditions in older adults that could be included in national health surveys. For this study, validated screening tools for frailty and malnutrition were identified to determine common risk measures; those found included functional measurements of gait speed and handgrip strength and self-reported health questions on exhaustion, activity level, unintentional weight loss, and appetite loss. Current national health surveys that included older adults were reviewed to determine inclusion of these measures. Analysis of 8 large national health surveys (NHANES, NHATS, NHIS, MCBS, NSOAAP, Medicare HOS, CPS-FSS, NHAMCS) revealed that while most surveys included at least one measure (i.e., unintentional weight loss), none contained all necessary screening data to properly monitor the prevalence of malnutrition and frailty risk in older adults. Having national data sets available on factors impacting functionality will help set national goals to support older adult independence, reduce related mortality and healthcare costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".