A study on predicting malnutrition risk in Parkinson’s disease patients using a nomogram model
Bibliographic record
Abstract
Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder that signif- icantly impacts the quality of life of affected individuals. Among the myriad of complications associated with PD, malnutrition has emerged as a critical concern, contributing to adverse clinical outcomes, including increased morbidity and mortality. Existing clinical assessments for identify- ing malnutrition, however, often lack the requisite precision and efficacy for early prediction, thus necessitating improved methodologies to address this gap. Methods: This study aimed to develop and validate a predictive nomogram model specifically de- signed for the early identification of malnutrition risk among individuals diagnosed with PD. Con- ducted between February 2022 and December 2023, this cross-sectional research enrolled a cohort of 163 patients from various inpatient and outpatient settings. Nutritional status was assessed using the Mini Nutritional Assessment (MNA) tool, while univariate and multivariate logistic regression analyses were employed to pinpoint critical risk factors contributing to malnutrition. Results: The analysis revealed several significant risk factors, including gender, body mass index (BMI), Gastrointestinal Symptom Rating Scale (GCSI) scores, Montreal Cognitive Assessment (MoCA) scores, and Barthel Index scores. The developed nomogram demonstrated an impressive area under the curve (AUC) of 0.92, with a sensitivity of 77.5% and specificity of 88%. Further- more, a cutoff risk score of 0.39 was established. Internal validation utilizing bootstrap methods yielded a concordance index (C-index) of 0.92, while calibration curves illustrated a strong align- ment between actual and predicted malnutrition risks. Conclusions: The notable prevalence of malnutrition among patients with PD accentuates the ur- gent need for effective screening tools. The validated nomogram model proposed in this study offers a promising approach for predicting malnutrition risk, ultimately aiming to enhance clin- ical outcomes within this vulnerable population. Future research may focus on integrating this nomogram into routine clinical practice to facilitate timely interventions and improve patient man- agement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".