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Record W3046202874 · doi:10.1002/alz.047682

Quantification of neurological blood‐based biomarkers in critically ill patients with COVID‐19

2020· article· en· W3046202874 on OpenAlexaff
Jennifer Cooper, Sophie Stukas, Ryan L. Hoiland, Sonny Thiara, Denise Foster, Anish R. Mitra, William J. Panenka, Mypinder S. Sekhon, Cheryl L. Wellington

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineDeliriumInternal medicineIntensive careModified Rankin ScaleIntensive care unitAnesthesiaIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract Background Multiple neurological manifestations of COVID‐19 have been reported such as headache, anosmia, ischemic stroke, and cerebral hemorrhages. Objective characterization of the acute neurological damage in critically ill patients with COVID‐19 has not yet been reported. Method We performed a prospective observational study of plasma brain biomarkers in critically ill patients with respiratory failure that were diagnosed with (COVID‐19) or without (ICU control) COVID‐19. Demographics, co‐morbidities, daily clinical physiologic and laboratory data were collected. Plasma samples were drawn for measurement of neurofilament‐light chain (NF‐L), total tau (t‐tau), ubiquitin carboxy‐terminal hydrolase L1 (UCH‐L1), and glial fibrillary acidic protein (GFAP). The primary neurological outcome was delirium as defined by the intensive care delirium screening checklist (ICDSC, scale 1 ‐ 8). Associations between brain biomarkers and markers of respiratory failure of COVID‐19 were analyzed. Result 27 patients with COVID‐19 and 19 ICU controls were enrolled. The concentration of plasma GFAP, UCH‐L1 and NF‐L levels was higher in both groups compared to healthy controls. Compared to ICU controls, patients with COVID‐19 had significantly higher GFAP (272 [150‐555] pg/ml vs 118 [78.5‐168] pg/ml, p=0.0009). In patients with COVID‐19, GFAP (rho=0.5115, p=0.0064), UCH‐L1 (rho=0.4056, p=0.0358) and NF‐L (rho=0.6223, p=0.0005) were positively correlated with the ICDSC score and were higher in patients diagnosed with delirium (ICDSC ≥4) in the COVID‐19 group but not ICU controls. There were no associations between PaO2/FiO2 or diagnosis of ARDS and plasma concentration of GFAP, t‐tau, UCH‐L1, or NF‐L in patients with COVID‐19. Conclusion Plasma GFAP is 2‐fold higher in critically ill patients with COVID‐19 compared to ICU controls, and higher concentrations of GFAP, UCH‐L1 and NF‐L are associated with delirium specifically in patients with 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)0.002
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.293
Teacher spread0.264 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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