Impact of social determinants of health on progression from potentially life-threatening complications to near miss events and death during pregnancy and post partum in a middle-income setting: an observational study
Bibliographic record
Abstract
OBJECTIVE: To assess the potential associations between social determinants of health (SDH) and severe maternal outcomes (SMO), to better understand the social structural framework and the contributory, non-clinical mechanisms associated with SMO. STUDY DESIGN: Prospective observational study. STUDY SETTING: Tertiary referral centre in south-eastern region of India. PARTICIPANTS: One thousand and thirty-three women with potentially life-threatening complications (PLTC) were identified using WHO criteria. RISK FACTORS ASSESSED: Social Determinants of Health (SDH). PRIMARY OUTCOMES: Severe maternal outcomes, which include maternal near-miss and maternal death. STATISTICAL ANALYSIS: Logistic regression to assess the association between SDH and clinical factors on SMO, expressed as adjusted ORs (aOR) with a 95% CI. RESULTS: Of the 37 590 live births, 1833 (4.9%) sustained PLTC, and 380 (20.7%) developed SMO. Risk of SMO was higher with increasing maternal age (adjusted OR (aOR) 1.04 (95% CI 1.01 to 1.07)), multiparity (aOR 1.44 (1.10 to 1.90)), medical comorbidities (aOR 1.50 (1.11 to 2.02)), obstetric haemorrhage (aOR 4.63 (3.10 to 6.91)), infection (aOR 2.93 (1.83 to 4.70)), delays in seeking care (aOR 3.30 (2.08 to 5.23)), and admissions following a referral (aOR 2.95 (2.21 to 3.93)). SMO was lower in patients from socially backward community (aOR 0.45 (0.33 to 0.61)), those staying more than 10 km from hospital (aOR 0.56 (0.36 to 0.78)), those attending at least four antenatal visits (aOR=0.53 (0.36 to 0.78)) and those referred from resource-limited facilities (aOR=0.62 (0.46 to 0.84)). CONCLUSION: This study demonstrates the independent contribution of SDH to SMO among those sustaining PLTC in a middle-income setting, highlighting the need to formulate preventive strategies beyond clinical considerations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".