Reducción de la exposición en residentes de Cirugía frente al brote de COVID-19
Bibliographic record
Abstract
The worldwide outbreak of COVID-19 during the first quarter of 2020 constitutes an unprecedented challenge for the health system. The aim is to describe the strategies adopted by residents of General Surgery of a university hospital of Argentina, to safeguard the health of residents, reduce the risk of exposure of surgical patients, maintain continuous academic training and promote teamwork. Minimize resident exposure by dividing the group into two teams that work by fortnights; divide activities, hours within hospital, and shifts equally among residents; use telemedicine for postoperative / ambulatory controls; suspend office activity; organize daily online classes and reviews of published articles. In the context of the COVID-19 pandemic, all means should be used to minimize the risk of exposure in order to optimize human resources. Although these strategies can easily be applied to other residencies, more research is needed to assess their impact on disease transmission, and on the physical and emotional health of health professionals
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".