Association between screening for antenatal depressive symptoms and delivery outcomes: The Born in Queensland Study
Bibliographic record
Abstract
BACKGROUND: Evidence shows that depressive symptoms during pregnancy increase the risk of an intervention during delivery (induction, the use of forceps or vacuum, and caesarean sections (CS)). Many women with depression during pregnancy are not identified and therefore will not receive appropriate follow up of their symptoms. We hypothesised that routine screening for depressive symptoms during pregnancy could reduce detrimental consequences of depressive symptoms on delivery outcomes. AIM: We explored the association between screening for depressive symptoms during pregnancy and delivery outcomes. MATERIALS AND METHODS: A cross-sectional analysis of state-wide administrative data sets. The population included all women who delivered a singleton in Queensland between the July and December of 2015. Logistic regression analyses were run in 27 501 women (93.1% of the total population) with information in all variables. The following were the main outcomes: onset of labour, CS, instrumental vaginal delivery, and all operative deliveries (including both CS and instrumental vaginal deliveries). RESULTS: Women who completed the screening had increased odds of a spontaneous onset of labour (adjusted odds ratio (aOR) 1.18; 95% CI 1.09-1.27) and decreased odds of an operative delivery (instrumental or CS) (aOR 0.88; 95% CI 0.81-0.96). Among women who had a vaginal delivery, those who completed the screening had decreased odds of having an instrumental delivery (aOR 0.84; 95% CI 0.74-0.97). Sensitivity analyses in women who did not have a formal diagnosis of depression showed similar results. CONCLUSION: Our findings suggest that screening may decrease interventions during delivery in women with depressive symptoms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".