[Characteristics of hospital care and its relationship to severe maternal morbidity in Medellín, Colombia].
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine whether there is an association between severe maternal mortality (SMM) and the characteristics of access to and use of obstetric services by the participating women. METHODS: A study of cases and controls was conducted in a group of 600 women who were attended during pregnancy or the puerperium between 2011 and 2012 by obstetric services located in Medellín, Colombia. The study considered cases (n = 150) in obstetric patients who met the criteria for SMM established by the surveillance system being used in Medellín at the time of their admission. The controls (n = 450) were randomly selected in the same institutions where the patients were being treated. The information was obtained through an in-person interview, review of the patient's clinical history, and rating of the medical care provided by surveillance program personnel. The analysis was based on the model Road Map for Preventing Maternal Death developed jointly by Pan American Health Organization/World Health Organization, Centers for Disease Control, United Nations Population Fund for Latin America and the Caribbean, and Mothercare UK. RESULTS: The proportion of unplanned pregnancies in the women studied was 57.6%, while the proportion of delay in the decision to seek care was 32.0%. Two variables were found to be associated with SMM: ethnicity (OR = 1.79) and delays due to deficiencies in the quality of care provided (OR = 8.54). CONCLUSIONS: The findings suggest that improving the effectiveness and quality of family planning, prenatal check-up, and hospital obstetric care programs could help to reduce avoidable cases of SMM.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".