Nomogram for predicting cognitive impairment in postpartum depression patients after pharmacotherapy: Development and validation
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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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".