Individual and institutional determinants of caesarean section in referral hospitals in Senegal and Mali: a cross-sectional epidemiological survey
Bibliographic record
Abstract
BACKGROUND: Two years after implementing the free-CS policy, we assessed the non-financial factors associated with caesarean section (CS) in women managed by referral hospitals in Senegal and Mali. METHODS: We conducted a cross-sectional survey nested in a cluster trial (QUARITE trial) in 41 referral hospitals in Senegal and Mali (10/01/2007-10/01/2008). Data were collected regarding women's characteristics and on available institutional resources. Individual and institutional factors independently associated with emergency (before labour), intrapartum and elective CS were determined using a hierarchical logistic mixed model. RESULTS: Among 86 505 women, 14% delivered by intrapartum CS, 3% by emergency CS and 2% by elective CS. For intrapartum, emergency and elective CS, the main maternal risk factors were, respectively: previous CS, referral from another facility and suspected cephalopelvic-disproportion (adjusted Odds Ratios from 2.8 to 8.9); vaginal bleeding near full term, hypertensive disorders, previous CS and premature rupture of membranes (adjusted ORs from 3.9 to 10.2); previous CS (adjusted OR=19.2 [17.2-21.6]). Access to adult and neonatal intensive care, a 24-h/day anaesthetist and number of annual deliveries per hospital were independent factors that affected CS rates according to degree of urgency. The presence of obstetricians and/or medical-anaesthetists was associated with an increased risk of elective CS (adjusted ORs [95%CI] = 4.8 [2.6-8.8] to 9.4 [5.1-17.1]). CONCLUSIONS: We confirm the significant effect of well-known maternal risk factors affecting the mode of delivery. Available resources at the institutional level and the degree of urgency of CS should be taken into account in analysing CS rates in this context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".