<i>Hospital Diagnosis of Malnutrition:</i> A Call for Action
Bibliographic record
Abstract
The Canadian Institute for Health Information (CIHI) provides accurate health information needed to establish sound health care policies. The CIHI mandate is to develop and co-ordinate a uniform approach to health care information in Canada. The institute uses the International Classification of Diseases (ICD) system to record the most responsible diagnosis for each hospital admission. This investigation was conducted to determine if six ICD protein-calorie malnutrition (PCM) codes could be used for health care utilization analyses. Aggregate data (1996 to 2000) from the CIHI discharge abstract database were used. The data analyzed were the most responsible diagnoses data for the six PCM codes and a single summary statistic for all other "non-malnutrition" diagnoses for all long-term care facility residents aged 65 or older who were transferred to an acute care facility. In this population, fewer than five hospital admissions per year were assigned a PCM code. There were too few PCM cases to do trend analyses for morbidity or mortality. This study suggests a lack of recognition and documentation of PCM as a specific health condition in older adults. Lack of tracking of this diagnosis prevents documentation that could lead to policy changes to support older adults' nutrition.
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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 | 0.031 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".