PREVALENCE AND BURDEN OF MALNUTRITION DIAGNOSIS AMONG OLDER ADULTS TREATED IN UNITED STATES EMERGENCY DEPARTMENTS
Bibliographic record
Abstract
Malnutrition is a treatable condition that when left untreated contributes to poor health outcomes and impacts recovery from illness and injury. Approximately 12% of older adults in US EDs suffer from malnutrition; while detection is improving in some care settings, it may still be low in the ED. We assessed the prevalence and burden of malnutrition diagnosis using national-level US data representing 138 million visits nationwide. ED visits for patients aged 65 years or older between October 2014 and September 2015 in the Healthcare Cost and Utilization (HCUP) Nationwide Emergency Department Sample (NEDS), the largest all-payer ED database in the US were analyzed. Malnutrition diagnosis was identified using International Classification of Diseases, 9th Edition, diagnosis codes. The economic burden was assessed by comparing the average total charges for ED visits, stratified by whether the patient was hospitalized. Multiple linear regression was used to adjust for confounding (demographics and Charlson Comorbidity Index). The prevalence of malnutrition diagnosis was 3.6% with a higher prevalence in urban and western geographical regions. ED visits with a diagnosis of malnutrition were associated with an average $3,949 (visits not resulting in hospitalization) or $25,575 (visits resulting in hospitalization) higher reported total charges than ED visits without a malnutrition diagnosis. Compared with the malnutrition prevalence reported in previous ED-specific studies (12%), the lower prevalence we observed suggests malnutrition may be under-diagnosed in older ED patients. Malnutrition among older adult patients poses a significant economic burden, highlighting the need for malnutrition screening and treatment protocols in US EDs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".