Recomendaciones para la prevención de la tosferina por la Sociedad Ecuatoriana de Microbiología Médica e Infectología Pediátrica (SEMMIP), Sociedad Ecuatoriana de Pediatría, Filial Pichincha (SEPP), Asociación de Neonatólogos de Ecuador (ASNEO Ecuador), Sociedad Ecuatoriana de Cuidados Intensivos Pediátricos (SECIP.EC) y Sociedad Ecuatoriana de Infectología – Núcleo Pichincha (SEI)
Bibliographic record
Abstract
La tos ferina continúa representando una amenaza significativa para la salud pública pediátrica, particularmente en lactantes menores de seis meses. A pesar de la disponibilidad de vacunas eficaces, se han reportado brotes recientes en América Latina, incluido Ecuador. Este documento, elaborado por cinco sociedades científicas ecuatorianas, presenta recomendaciones basadas en evidencia para la prevención de la tos ferina, adaptadas al contexto local. Se abordan esquemas de vacunación para niños, embarazadas, personal de salud y población general, así como la estrategia “capullo” para proteger a los recién nacidos. Además, se incluyen medidas generales de higiene respiratoria y aislamiento. La implementación integral de estas estrategias busca reducir la morbilidad y mortalidad asociadas a Bordetella pertussis y fortalecer la respuesta nacional frente a esta enfermedad prevenible por vacunación.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".