Predicting Conversion to Insulin Sensitivity With Metformin
Bibliographic record
Abstract
BACKGROUND: Insulin resistance (IR) changes the trajectory of responsive bipolar disorder to a treatment-resistant course. A clinical trial conducted by our group demonstrated that IR reversal by metformin improved clinical and functional outcomes in treatment-resistant bipolar depression (TRBD). To aid clinicians identify which metformin-treated TRBD patients might reverse IR, and given strong external evidence for their association with IR, we developed a predictive tool using body mass index (BMI) and homeostatic model assessment-insulin resistance (HOMA-IR). METHODS: The predictive performance of baseline BMI and HOMA-IR was tested with a logistic regression model using known metrics: area under the receiver operating curve, sensitivity, and specificity. In view of the high benefit to low risk of metformin in reversing IR, high sensitivity was favored over specificity. RESULTS: In this BMI and HOMA-IR model for IR reversal, the area under the receiver operating curve is 0.79. At a cutoff probability of conversion of 0.17, the model's sensitivity is 91% (95% confidence interval [CI], 57%-99%), and the specificity is 56% (95% CI, 36%-73%). For each unit increase in BMI or HOMA-IR, there is a 15% (OR, 0.85; 95% CI, 0.71-0.99) or 43% (OR, 0.57; CI, 0.18-1.36) decrease in the odds of conversion, respectively. CONCLUSIONS: In individuals with TRBD, this tool using BMI and HOMA-IR predicts IR reversal with metformin with high sensitivity. Furthermore, these data suggest early intervention with metformin at lower BMI, and HOMA-IR would likely reverse IR in TRBD.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".