Bibliographic record
Abstract
El propósito del presente informe es comunicar al equipo de salud la gravedad que reviste el aumento de la sífilis materna y congénita, a lo cual contribuyen múltiples factores que es imprescindible reconocer para proponer estrategias de control. Una investigación realizada en el Centro Hospitalario Pereira Rossell (CHPR), por el Programa Nacional de Salud de la Niñez del Ministerio de Salud Pública (MSP) en colaboración con el Centro Latinoamericano de Perinatología-Salud de la Mujer y Reproductiva (CLAP/SMR) de la Organización Panamericana de la Salud durante el año 2007, puso en evidencia aspectos críticos sobre los que se deben focalizar las intervenciones. El Sistema Informático Perinatal (SIP) del CLAP/SMR en el CHPR, registró en los últimos años una tendencia creciente de gestantes y recién nacidos (RN) con VDRL reactivos (tabla 1). Con el fin de investigar los factores vinculados a la infección se realizó una revisión de historias clínicas y auditorias de esas mujeres, pudiendo rescatar un total de 142 historias de sífilis materna. Se analizaron diferentes variables tales como edad materna, paridad, antecedentes de sífilis, edad gestacional en el primer control, número de controles prenatales, tamizaje con VDRL, tratamiento oportuno, identificación y tratamiento de los contactos sexuales.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".