Variations in caesarean section outcome reporting in low- and middle-income countries: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To explore the variation and quality of the outcomes reported using descriptive analysis and interactive network visualisation of CS outcomes of comparative studies conducted in low-and middle-income settings. STUDY DESIGN: Systematic review of comparative studies to reduce caesarean section complications in low- and middle-income countries and outcome assessment using the modified Harman questionnaire, assessing for the presence of a clear primary outcome, an explanation of how the outcomes were analysed and a description of the methods used to enhance the quality of these measures. RESULTS: 102 comparative studies were included. Studies of interventions to improve maternal and perinatal outcomes after caesarean section reported 466 outcomes with 15 % of these outcomes appearing only once across the outcomes reviewed (n = 73). The most common outcome categories reported were maternal death, disability and bleeding. Psychological and injury outcomes were less commonly reported. The overall quality of outcome reporting varied between studies but was particularly low for reporting on methods to improve outcome measures. Very few outcomes scored a maximum of three points when assessed according to the modified Harman score, with only 15 of the primary outcomes (16 %) achieving 3 points and 40 of the secondary outcomes (11 %) achieving 3 points. The median quality of reporting was 2 (range 0, 3) for all outcomes, for a maximum score of 3. Quality of outcome reporting was associated with the type of outcome (primary or secondary), the region in which the study was conducted in, and journal characteristics such as impact factor and journal type. CONCLUSIONS: There was wide variability in both the outcomes reported and the frequency in which they were reported. Overall, very few primary and secondary outcomes achieved the maximum score of three on the modified Harman score, highlighting the need for a core outcome set for caesarean section intervention trials to improve the consistency and synergy of future research outcome definition, measurement and synthesis. TRIAL REGISTRATION: The protocol was registered (PROSPERO CRD42022353939).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".