Trayectorias sanitarias de mujeres consumidoras de cocaína y/o cannabis durante el embarazo. Estudio de cohorte retrospectivo en La Pampa, Argentina
Bibliographic record
Abstract
Introduction: the use of cocaine and cannabis during pregnancy is a growing problem, of importance for the World Public Health. Women can present a wide range of complications during pregnancy and perinatally, although knowledge about evolution thereafter is scarce. Objectives: to describe and compare health trajectories up to 4 years pospartum of user and not user women of cocaine and/or cannabis during pregnancy. Methods: retrospective cohort study with a comparison group consisting of 2 not users women for each user during pregnancy. User women were detected in Neonatology Division of a public Hospital with an urine pospartum test, between 2009 and 2013. Results: 29 women cocaine and/or cannabis users during pregnancy, and 58 not users were evaluated. In the first group, 93% women were polydrug users. Users had higher frequency of: history of violence in childhood ((p<0,001), previous (p<0,0045) and acquired (p<0,0007) disease, medical (p<0,0001) and odontological (p=0,0002) emergency consultations, hospitalizations (p<0,0001), and violent deaths in relatives, compared with non users. Conclusions: women who used cocaine and/or cocaine during pregnancy had a greater history of violence and illness, emergency medical and dental consultation, hospitalization and deaths, than non users. Injuries where the main cause of hospitalization. In the variables evaluated, the differences between consumers and non-consumers of pregnancy were significative.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".