Abstract 16347: The Association of Depressive Symptoms and the Intermountain Mortality Risk Score to All-cause Mortality
Bibliographic record
Abstract
Introduction: The patient health questionnaire (PHQ)-9 is widely used for the diagnosis and assessment of depression severity, but further study is needed regarding whether it provides prognostic information for major events such as mortality. The sex-specific Intermountain Mortality Risk Score (IMRS©) is a widely-validated risk stratification tool that utilizes the complete blood count (CBC), basic metabolic profile (BMP), and age to predict all-cause mortality. Hypothesis: IMRS is associated with all-cause mortality among subgroups defined by depressive symptoms. Methods: Patients who completed a PHQ-9, were ≥40 years, and had a CBC and BMP tested as part of their clinical care were studied. Prior validated sex-specific IMRS weightings and risk stratifications of low, moderate, and high were used. Patients were stratified by depressive symptoms of none (PHQ-9 score <10), mild (PHQ-9 score 10-14), and moderate to severe (PHQ-9 score ≥15). Multivariable Cox hazard regression was performed among all patients and within strata defined by depressive symptoms to determine associations of IMRS and depressive symptoms with all-cause mortality. Results: A total of 11,583 females (age: 58.7±12.7 years) and 7,814 males (age: 59.9±12.5 years) were evaluated. Both PHQ-9 and IMRS stratifications were associated with all-cause mortality in a step-wise manner, which persisted despite multivariable adjustment (Table). In categories defined by depressive symptoms, IMRS markedly stratified risk of mortality (Table). Conclusion: IMRS and severity of depression were independent predictors of mortality risk among patients screened for depression. Further, IMRS was a powerful predictor of mortality within each level of depressive symptoms. This study suggests a need to evaluate IMRS as a tool for informing clinical risk stratification among patients with symptoms of depression and directing additional resources to those at highest risk of major adverse events.
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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 | 0.002 |
| 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) | 0.004 | 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".