The PPD-ACT App: Feasibility of a mobile application for recruiting women with postpartum depression and psychosis to a psychiatric genetics study (Preprint)
Bibliographic record
Abstract
BACKGROUND Postpartum depression (PPD) is linked to long-standing negative consequences for women and families. Virtual care applications present a solution to the challenge of recruiting large samples for genetic PPD research. OBJECTIVE This study evaluated the feasibility of using an iOS mobile application (PPD-ACT app) to collect genetic samples from Canadian women with current or past PPD. METHODS Women downloaded the PPD-ACT app to provide consent, phenotypic data, and if they met inclusion criteria, mailing information for a genetic sample. PPD was defined as: (1) Edinburgh Postnatal Depression Scale (EPDS) score of >12 at most severe episode, (2) onset in pregnancy or 0-3 months postpartum, and (3) no preterm birth or severe maternal or child illness. Latent class analysis (LCA) described PPD case phenotypes. Women self-reporting lifetime postpartum psychosis (PPP) were also asked to provide a DNA sample. RESULTS Of 797 women meeting PPD case criteria, 75.0% (n=598) agreed to be sent a DNA collection kit; 67.6% of whom (n=404) submitted a sample. Women (86.7% White) came from 11/13 Canadian provinces/territories, with a mean of 4.7 years (SD=7.0) since the PPD episode. LCA identified two PPD classes, clinically distinct by illness severity: (1) moderate severity (mean EPDS=18.5, SD=2.5; 8.6% with suicidality), and (2) severe (mean EDPS=24.5, SD=2.1; 52.8% with suicidality). 109 women self-reported a history of PPP (39 without PPD; 60.7% submitted DNA). CONCLUSIONS A mobile application rapidly collected data from women with relatively severe forms of PPD, an advantage for genetics where specificity is optimal, and may be feasible for collecting data from women with a history of the even more rare condition of PPP. Expanding the mobile platform options might increase accessibility as well as the diversity of the sample.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".