Enfermedad por coronavirus (COVID-19) en embarazo, parto y lactancia
Bibliographic record
Abstract
La pandemia de COVID-19 ha generado múltiples interrogantes respecto a su comportamiento en la población gestante y en los resultados perinatales. Los datos disponibles sobre la infección por SARS-CoV-2 en el embarazo son limitados. Se realizó una búsqueda de artículos publicados en las bases de datos PubMed, Scopus y Embase utilizando los términos asociados a COVID-19 y embarazo, hasta el 4 de abril de 2020. En la revisión de 43 artículos se tuvieron en cuenta 25, que corresponden a reportes y series de caso, revisiones y guías de manejo. No se encontró evidencia concluyente respecto a transmisión vertical o a mal resultado perinatal en enfermedad leve o moderada. Los síntomas clínicos de COVID-19 en el embarazo, no varían de los de la población general. Existe controversia en cuanto a lactancia materna. En conclusión, existe escasa evidencia de calidad sobre el efecto de COVID-19 en el embarazo. Dada la ausencia de evidencia concluyente se plantea la realización de un registro nacional de COVID-19 y embarazo para Colombia y la región.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".