Bibliographic record
Abstract
BACKGROUND: Cesarean delivery rates are increasing rapidly in many developing countries, particularly among wealthy women. Poor women have lower rates, often so low that they do not reach the minimum rate of 1 percent. Little data are available on clinical indications for cesarean section, information that could assist in understanding why cesarean delivery rates have changed. This paper presents recommendations for routine reporting on indications for cesarean delivery in developing countries. These recommendations resulted from an international consultation of researchers held in February 2006 to promote the collection of comparable data to understand change in, or composition of, the cesarean delivery rate in developing countries. METHODS: Data are presented from selected countries, categorizing cesareans by three classification systems. RESULTS: A single classification system was recommended for use in both high and low cesarean delivery rate settings, given that underuse and overuse of cesarean section are evident within many populations. The group recommended a hierarchical categorization, prioritizing cesareans performed for absolute maternal indications. Categorization among the remaining nonabsolute indications is based on the primary indication for the procedure and include maternal and fetal indications and psychosocial indications, required for high cesarean delivery rate settings. CONCLUSIONS: Data on indications for cesarean sections are available everywhere the procedure is performed. All that is required is compilation and review at facility and at higher levels. Advocacy within ministries of health and medical professional organizations is required to advance these recommendations since researchers have inadequately communicated the health effects of both underuse and overuse of cesarean delivery.
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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.081 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".