Consultas vía WhatsApp en un servicio de electrofisiología de un hospital público de la Ciudad de Buenos Aires en tiempos de COVID-19
Bibliographic record
Abstract
Introducción: La pandemia por coronavirus (COVID-19) es altamente contagiosa. La telemedicina emerge como una opción para mantener a nuestros pacientes dentro del sistema sanitario. Objetivo: Implementar consultas por WhatsApp durante 30 días en un hospital de la Ciudad Autónoma de Buenos Aires (CABA) durante la cuarentena impuesta por COVID-19. Material y métodos: Se analizaron consultas por WhatsApp durante 30 días consecutivos. Se envió un formulario antes de la consulta telefónica con el especialista. Se realizó un análisis descriptivo de las consultas y los planes propuestos para el seguimiento. Resultados: Se realizaron 263 consultas en 205 pacientes. La cantidad promedio de consultas telefónicas fue de 7,8 mensajes. Las consultas más frecuentes fueron: palpitaciones (12%) y vacunación antigripal (11,7%). El seguimiento quedó dividido en grupos: 1) Resueltos vía WhatsApp: 154 pacientes; 2) Derivados a un hospital zonal: 25; 3) Derivados a nuestro hospital: 26 pacientes. Conclusión: La telemedicina vía WhatsApp es factible de ser desarrollada en un hospital público de la CABA, con una sustancial reducción de consultas presenciales.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 0.001 |
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".