Electronic Measurement-based care (eMBC) for perinatal depression and anxiety: a pilot randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: As few as 20% of perinatal patients with depression or anxiety are treated to remission. Measurement-based care (MBC) improves patient outcomes but has not been evaluated for perinatal mental illness. We aimed to assess the feasibility of an MBC protocol in perinatal patients experiencing depression and/or anxiety symptoms. METHODS: In this pilot randomized controlled trial (RCT), perinatal people with Edinburgh Postnatal Depression Scale (EPDS) scores ≥ 13 were randomized 1:1 to (1) an electronic MBC (eMBC) intervention embedded in an electronic health record (EHR) that included scales assessing symptoms and functioning at each clinical visit or (2) usual care, for 12 weeks post-randomization. The primary outcome was feasibility (recruitment, acceptability, trial protocol adherence). While not powered to detect clinically significant differences on clinical outcomes, we also measured depressive and anxiety symptoms (Montgomery-Asberg Depression Rating Scale, MADRS; Hamilton Anxiety Scale, HAM-A). RESULTS: Of 42 participants (n = 21/arm), 32 (76.2%) completed follow-up questionnaires. At least one scale was completed in 87.5% of clinical encounters, but only 68.8% of encounters included documented participant-provider discussion of the results. Acceptability was good, with opportunities identified for improvement from participant and provider perspectives. At 12-weeks post-randomization, MADRS and HAM-A scores were non-signficantly lower in the eMBC group (mean differences: -1.10, 95%CI -7.81 to 5.61; -1.28, 95%CI -4.69 to 2.12). CONCLUSIONS: The protocol evaluated in this study was feasible, which supports proceeding to a larger RCT to evaluate efficacy with minor modifications. If effective, an EHR-integrated eMBC intervention for perintal depression and anxiety could be implemented widely. TRIAL REGISTRATION: The trial was registered at www. CLINICALTRIALS: gov (NCT04836585). Registration Date: 08/04/2021.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".