Abstract 12709: Addition of Depression Severity Indices to the Intermountain Risk Score for Predicting 3-Year Risk of Chronic Disease: The Mental Health Integration Risk Score
Bibliographic record
Abstract
Introduction: Depression (DPN) is a common illness that imposes a disproportionately large health burden. DPN is generally associated with higher prevalence of chronic disease (ChrD) risk factors and may contribute higher ChrD risk. Previously, the Intermountain Mortality Risk Score (IMRS) was shown to predict DPN incidence and post-diagnosis outcomes. Hypothesis: Sex-specific Mental Health Integration risk scores (MHIRS) can be developed based on the principles underlying IMRS and adding DPN severity indices to predict 3 year ChrD diagnosis. Methods: MHIRS was created to predict the first diagnosis of any of 10 ChrD (diabetes, renal failure, CAD, MI, HF, PVD, AF, stroke, dementia, or COPD) in patients completing a PHQ-9 DPN survey and were free at baseline from those 10 ChrD diagnoses. MHIRS used sex-specific weightings of PHQ-9 results, age, and components of the comprehensive metabolic panel and complete blood count in randomly-chosen derivation (70%) and validation (30%) groups. Results: Females (N=10,162; age: 48±16) and males (N=4615, age: 48±15) were studied. Among females, c-statistics for the composite ChrD endpoint were 0.746 (0.725, 0.767) for the derivation group and 0.717 (0.682, 0.753) for the validation group, while males had 0.755 (0.727, 0.783) and 0.742 (0.702, 0.782). See Table for MHIRS prediction of each ChrD. In the validation group, MHIRS strata of low, moderate, and high risk had hazard ratios (HR) for any 3-year ChrD diagnosis among females of HR=3.42 for moderate vs. low and HR=9.75 for high vs low, while males had HR=4.80 and HR=10.68, respectively (all p<0.0001). Conclusion: A clinical decision tool composed of DPN severity and common lab tests, MHIRS provided very good discrimination of a 3 year ChrD diagnosis. Designed to be calculated electronically by an electronic health record, MHIRS can be efficiently obtained by clinicians to identify patients at higher ChrD risk who require further evaluation and more precise clinical management.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".