Nomogram for predicting cognitive impairment in postpartum depression patients after pharmacotherapy: Development and validation
Why this work is in the frame
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Bibliographic record
Abstract
Objective: To develop and validate a clinical feature-based nomogram for predicting the risk of significant cognitive impairment in postpartum depression (PPD) patients treated with selective serotonin reuptake inhibitors (SSRIs). Methods: A retrospective study was conducted on 350 PPD patients treated with SSRI monotherapy at our institution between January 2020 and December 2024. Cognitive function was assessed at week 8 using the Montreal Cognitive Assessment (MoCA), with MoCA < 26 defined as significant cognitive impairment. Predictors were screened using LASSO regression, and a multivariate logistic regression model was built to construct the nomogram. Internal validation was performed via bootstrapping (1000 repetitions). Model discrimination, calibration, and clinical utility were evaluated using the C-statistic, calibration curve, and decision curve analysis (DCA). Results: Six predictors were identified: age, baseline depression severity (HAMD-17 score), years of education, SSRI type (paroxetine vs. others), baseline sleep quality (PSQI score), and postpartum duration. The model exhibited a C-statistic of 0.82 (95% CI: 0.78–0.86). The calibration curve demonstrated good agreement between predicted and actual risks. DCA indicated significant clinical net benefit across a wide threshold probability range (0.1–0.6). Conclusion: This nomogram effectively predicts individualized risk of significant cognitive impairment in SSRI-treated PPD patients, demonstrating good discrimination, calibration, and clinical utility. It serves as a valuable tool to aid clinical decision-making.
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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 | 0.000 | 0.000 |
| 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 it