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Record W4385649371 · doi:10.1080/09273948.2023.2238817

Postmortem Ultrastructural Analysis of the Retina from COVID-19 Deceased Patients

2023· article· en· W4385649371 on OpenAlexaff
Carlla Assis Araújo-Silva, Paula M. Marinho, Allexya Affonso Antunes Marcos, Ana M. C. Branco, Victoria Sakamoto, Mateus Matuoka, Nara Franzin de Moraes, Paulo F. G. M. M. Tierno, Walid Mourad, Heloísa Nascimento, Miguel N. Burnier, Wanderley de Souza, Rubens Belfort Neto

Bibliographic record

VenueOcular Immunology and Inflammation · 2023
Typearticle
Languageen
FieldMedicine
TopicRetinal and Optic Conditions
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineRetinaCoronavirus disease 2019 (COVID-19)PathologyRetinalSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)UltrastructureCoronavirus2019-20 coronavirus outbreakOphthalmologyDiseaseInfectious disease (medical specialty)BiologyOutbreak

Abstract

fetched live from OpenAlex

PURPOSE: COVID-19 (coronavirus disease 2019) is an infectious disease caused by SARS-CoV-2, first reported in 2019 in Wuhan, China. Among the common complications is a pro-inflammatory and hypercoagulative response that compromises the vasculature among various organs. METHODS: In this report, we present the postmortem retinal findings of five patients observed by means of optical microscopy and transmission and scanning electron microscopy techniques. RESULTS: Clinical manifestations such as retinal hemorrhages and exacerbated inflammatory infiltrate, altered ultra structure with swollen mitochondria and pyknotic cells in both layers of the retina were observed in all analyzed eyes. CONCLUSION: Our data point to the fragility of this tissue in cases of severe COVID-19.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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