Recomendaciones generales apra mejorar la calidad de la atención obstétrica
Bibliographic record
Abstract
INTRODUCTION. Maternal care means roughly a half of medical\ninterventions, hospital discharges and surgery realized in Mexico. Obstetrics\nreached the second place (14.5%) in malpractice complaints in National\nCommission of Medical Arbitration (CONAMED). OBJECTIVE: To analize the\nCONAMED�s experience about obstetric care claims, and profit\nrecommendations for medical practice. METHOD: The authors revised 1431\ncomplaints ob/gin-related, placed in CONAMED between june 1996 to\njune 2001, and selected a sampling of 121 cases of ruling reports. We\ndescribe sociodemographic, institutional, clinical, communication and lawattach\nindicators. RESULTS. Complaints were originated in third quarter of\npregnancy (82.8%), in social-security services (72.1%), in second level\nhospitals (62.3%). Fifty seven percent were high risk pregnancies, with\nprevious cesarean section as frequent medical historial (21.5%). The\ncomplications were predictable in almost half of cases. First medical error\nwas a deficient labor surveillance (22.3%). Malpractice was identified in\n54.5%, ethical mistakes in 30%, institutional insufficiencies in 40.5%,\ninaccurate medical records in 45% and inadequate communication in 76%.\nCONCLUSIONS. An accurate obstetric care is composed by proffesional\nsense of duty and ethics, conscientious patient care, identify high risk\npregnancies, recognize personal and institutional skill restrictions, to know\nhealth laws, and effective physician - patient communication. We declare\n9 recommendations for medical and paramedical who care obstetric\npatients.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".