WhatsApp Consultations in the Department of Electrophysiology of a Public Hospital of the City of Buenos Aires in Times of COVID-19
Bibliographic record
Abstract
Background: Coronavirus (COVID-19) pandemic is highly infectious. Telemedicine emerges as an option to keep patients within thehealthcare system.Objective: The aim of this study was to implement WhatsApp consultations during 30 days in a hospital of the City of Buenos Aires(CABA) during the lockdown imposed due to COVID-19.Methods: Consultations via WhatsApp were analyzed for 30 consecutive days. A form was sent prior to telephone consultation withthe specialist. A descriptive analysis of consultations and proposed follow-up plans was carried out.Results: A total of 263 consultations were performed in 205 patients. The average number of telephone consultations was 7.8 messages.The most common topics for consultation were palpitations (12%) and influenza vaccine (11.7%). Follow-up was divided intogroups: 1) Solved via WhatsApp: 154 patients; 2) Referred to a local hospital: 25; 3) Referred to our hospital: 26 patients.Conclusion: Telemedicine via WhatsApp can be developed in public hospitals of CABA, with a substantial reduction of in-personconsultations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".