Task shifting cesarean sections in low‐ and middle‐income countries: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Safe and timely access to cesarean section (CS) in low‐ and middle‐income countries (LMIC) remains a significant challenge. Objectives To compare maternal and perinatal outcomes of CS by non‐physician clinicians (NPCs) versus physicians in LMIC. Search strategy and Selection criteria A systematic search of Ovid MEDLINE, EMBASE, Cochrane Library (including CENTRAL), Web of Science, and LILACS was performed from inception to January 2022. Data collection and analysis Data were extracted by two independent reviewers and meta‐analysis was performed when possible. Main results Ten studies from seven African countries were included. There was no significant difference in maternal mortality for CS performed by NPCs versus physicians (odds ratio [OR] 1.09, 95% confidence interval [CI] 0.56–2.14, P = 0.8, I2 = 70%, P < 0.05, eight studies, n = 20 711) or in perinatal mortality (OR 1.18, 95% CI 0.86–1.61, P = 0.3, I2 = 88%, n = 19 716). Despite heterogeneous clinical settings between providers, there was no difference in the rates of wound infection or re‐operation, although there was a higher rate of wound complications (such as dehiscence) in the NPC group (OR 1.89, 95% CI 1.21–2.95, P = 0.005, n = 6507). Conclusions NPCs have comparable maternal and neonatal outcomes for CS compared with standard providers, albeit with increased odds of wound complication. PROSPERO Registration CRD42020217966.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".