Es posible mejorar la salud a través de las TICs: Alejandro Jadad, Director de Global eHealth & Wellness Network Initiative (geni), Universidad de Toronto
Bibliographic record
Abstract
Alejandro Jadad, nacido en 1963 en Monteria (Colombia), es medico especializado en anestesiologia por la Universidad Javeriana de Bogota y Doctor en manejo del dolor por la Universidad de Oxford. En 1995 se traslada a Canada, donde desempena el cargo de Director del Centro de Practica Basada en Evidencias de la Universidad de McMaster. En el ano 2000 crea el Centro para Innovaciones Electronicas en Salud de la Universidad de Toronto, donde se convierte en Catedratico en cuidados de apoyo para pacientes con problemas serios de salud y en innovaciones electronicas en salud. Actualmente preside el Instituto de Innovacion para el Bienestar del Ciudadano. La entrevista tuvo lugar en el marco del II Encuentro Sociedad del Conocimiento y Ciudadania, celebrado en Malaga los dias 29 y 30 de noviembre de 2007.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".