Promovendo o bem comum em tempos de COVID-19: a perspectiva da Inovação Responsável em Saúde
Bibliographic record
Abstract
A pandemia de COVID-19 um dos problemas de sade pblica mais graves das ltimas dcadas. Em 10 de junho de 2020, aproximadamente 7,3 milhes de casos e 413 mil mortes haviam sido oficialmente notificados no mundo todo 1 . Embora muitos pases tenham conseguido achatar a curva de infeco pelo SARS-CoV-2, a situao particularmente grave na Amrica Latina, regio considerada recentemente o novo epicentro da pandemia segundo a Organizao Mundial da Sade. Diante desse cenrio, agravado pela inexistncia de vacina ou tratamento eficaz contra a COVID-19, diversas solues tm sido propostas para o seu enfrentamento. Tais solues incluem testes para rastreamento e confirmao diagnstica 2 , intervenes no farmacolgicas de alcance individual, ambiental e comunitrio para reduzir a velocidade de transmisso do vrus 3 e tecnologias para
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".