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Record W3188115742 · doi:10.25132/raac.v112.n2.1492.es

Reducción de la exposición en residentes de Cirugía frente al brote de COVID-19

2020· article· en· W3188115742 on OpenAlexaboutno aff
Agustín Salvador Valido Morales, Mora Achával, Juan Cruz López Meyer, Carla Gonzales Vega, Gerónimo Faillace, Guadalupe Iudica, Ezequiel Verde, Carina Chwat

Bibliographic record

VenueRevista Argentina de Cirugía · 2020
Typearticle
Languageen
FieldPsychology
TopicStress and Burnout Research
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Context (archaeology)MedicineTeamworkPandemicNursingQuarter (Canadian coin)DiseasePolitical scienceInfectious disease (medical specialty)Geography

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.045
GPT teacher head0.408
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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