Development of the screening for malnutrition risk tool for home care (smart-HC)
Bibliographic record
Abstract
This thesis investigates screening for malnutrition risk among seniors receiving home care using items within the Resident Assessment Instrument for Home Care (RAI-HC). The purpose of this study was to develop an embedded malnutrition risk screening tool (SMART-HC) targeted to community-dwelling seniors as an alternative to the existing RAI-HC malnutrition triggers (NutnCAP). Potential malnutrition risk factors identified from three focus groups of case managers from community care access centers (CCACs) in southern Ontario, and five key informant interviews with registered dietitians (RD), were triangulated against potential risk factors from the RAI-HC identified by five RD's who participated in a nominal group. "Change" was identified as a catalyst for malnutrition risk. Logistic regression (LR) and decision tree analysis (DT) (SAS version 9.1) were used to identify which of the identified 48 RAI-HC items were associated with malnutrition risk, defined as the presence of any of: unintentional weight loss, cachexia or decreased food intake. The best subsets of items that could be used to develop the SMART-HC were determined. A conceptual model for malnutrition risk, a five level algorithm (Level 1, no risk to Level 5, very high risk) and a summed index (0-15) were developed. SMART-HC high-risk levels are comprised of the following malnutrition indicators: reduced food and fluid intake, loss of appetite, dysphagia, end-stage disease, unintentional weight loss, cachexia and insufficient fluid intake. SMART-HC low and moderate risk levels are comprised of the following malnutrition risk factors: health status, functional ability, self-reported poor health, mood status, social function and cognitive performance. The proportions of clients classified at high risk by SMART-HC (26% to 30%) were higher than compared to the NutnCAP (21%) and to clients referred to a dietitian (RDref) (8.2%). The DT and LR models for malnutrition risk were comparable and reproducible in three data sets. The SMART-HC for clients at high malnutrition risk had high sensitivity and fair specificity when compared to two reference standards (NutnCAP: sensitivity 90%; specificity 58% and RDref: sensitivity 91.5%; specificity 50%). The negative predictive value of the SMART-HC was 93%. Further research is required to establish the construct and predictive validity of the SMART-HC.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".