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Record W3132414893 · doi:10.1101/2021.02.18.21251442

Visuospatial processing impairment following mild COVID-19

2021· preprint· en· W3132414893 on OpenAlexaboutno aff
Jonas Jardim de Paula, Rachel Elisa Rodrigues Pereira de Paiva, Danielle de Souza Costa, Nathália Gualberto Souza e Silva, Daniela Valadão Rosa, José Nélio Januário, Luciana Costa Silva, Débora Marques de Miranda, Marco Aurélio Romano‐Silva

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsCoronavirus disease 2019 (COVID-19)NeuropsychologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Significant difference2019-20 coronavirus outbreakRecallPandemicNormativeMedicineCoronavirusPercentileQuarter (Canadian coin)PsychologyAudiologyClinical psychologyDiseaseCognitionPsychiatryInternal medicineStatisticsVirologyCognitive psychologyInfectious disease (medical specialty)Mathematics

Abstract

fetched live from OpenAlex

Abstract Severe Acute Respiratory Syndrome Coronavirus 2 infection causes coronavirus disease 2019. COVID-19 was an unknown infection that reached pandemic proportions in 2020 and has shown to bring long-term negative consequences. Here, we used a case-control design to investigate the performance of relatively young people recovered from COVID 19 in objective neuropsychological tests. We found significant differences between groups for all measures of the ROCFT with a large difference in the copy, a moderate difference in immediate recall, and a large difference in delayed recall. No significant differences were found for the measures from all the other five neuropsychological tests used.About one quarter of COVID 19 patients were below the 10th percentile according to normative data.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.026
GPT teacher head0.347
Teacher spread0.321 · 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 designObservational
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

Citations5
Published2021
Admission routes1
Has abstractyes

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