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Record W4378839232 · doi:10.51161/conbrapah2023/19103

O uso da ivermectina no tratamento da Covid-19: uma revisão da literatura

2023· article· pt· W4378839232 on OpenAlexaff
Bruno Feitosa Espino, Pedro Paulo de Oliveira Carneiro, Juliana Maria Gil Braz Piñeiro Guedes, Larissa Bandeira Santos Silveira, Michele Marques Gama Lorenzo

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldHealth Professions
TopicHealthcare Regulation
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Computer scienceMedicineInternal medicineDisease

Abstract

fetched live from OpenAlex

severa (SARS), com repercusso mundial em 2020, na sade da populao e economia global, gerou uma grande problemtica emtorno de uma nova afeco de sade sem tratamento efetivo, com alta infectividade. Nesse contexto, com a escassez de medicamento e a necessidade de terapias efetivas, a ivermectina destaca-se pela sua ao antiviral, como possvel forma de tratamento medicamentoso.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.228
GPT teacher head0.512
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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