<i>Assessing Nutritional Risk</i>of Long-Term Care Residents
Bibliographic record
Abstract
The validity was determined for Minimum Data Set (MDS) 2.0 oral/nutrition status (Section K) items, used to identify long-term care residents at nutritional risk. A registered dietitian assessed 128 long-term care residents using standardized procedures, and used clinical judgment to provide a nutritional risk rating. Registered nursing staff completed the MDS assessments. Bivariate tests of association were used to assess the relationship between the dietitian rating and each Section K item. The sensitivity (Se) and specificity (Sp) of specific and combinations of variables were also determined. The MDS variables of dietary prescription (diet rx), supplement use, and swallowing problems were significantly associated with nutritional risk rating. Body mass index (BMI), calculated from MDS data, also was significantly associated with nutritional risk rating. The MDS trigger system, however, had poor Se and Sp. The best combination of variables included the presence of one or more of diet rx, supplement use, swallowing problem, or BMI <24 kg/m2 (Se=0.81, Sp=0.50). Although Section K items are associated with nutritional risk, Se and Sp analyses suggest that these items and this section require further refinement and validation before use as part of a referral mechanism.
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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 | 0.018 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".