La Neonatología, primera Área de Capacitación Específica de la Pediatría en España
Bibliographic record
Abstract
El pasado mes de agosto del 2014, el Boletín Oficial del Estado publicó el Real Decreto 639/2014, de 25 de julio, por el que se regulan la troncalidad, la reespecialización troncal y las áreas de capacitación específica (denominadas «subespecialidades» en Europa).La Pediatría mantiene su formación independiente de otros troncos.Respecto a las Áreas de Capacitación Específica (ACE), se ha creado la de Neonatología con acceso único desde la especialidad de Pediatría 1 .Las ACE tienen un interés preferentemente asistencial pero requieren de una vertiente docente imprescindible para la correcta formación del subespecialista, en nuestro caso el neonatólogo.La enseñanza y la formación en neonatología están bien organizadas en Australia, Estados Unidos y Canadá.En Europa, el European Board of Pediatrics reconoció en 1997 al Working Group in Neonatology (WGN) de la European Society for Pediatric Research (ESPR).En agosto del 2001, el WGN-ESPR se convirtió en la European Society for Neonatology (ESN), reconocida por la Confederation of European Subspecialities in Pediatrics (CESP) como la organización representativa de todos los neonatólogos europeos 2 .La ESN ha elaborado un programa de formación para los neonatólogos en Europa con el objetivo de armonizar los programas de formación entre los distintos países
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".