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Record W4210775271 · doi:10.1016/j.bpsgos.2022.01.006

Systemic Inflammatory Biomarkers in DSM-5–Defined Disorders and COVID-19: Evidence From Published Meta-analyses

2022· review· en· W4210775271 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiological Psychiatry Global Open Science · 2022
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoCanada Research Chairs
KeywordsCoronavirus disease 2019 (COVID-19)MedicineOutbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)C-reactive proteinInterleukinPandemicInterleukin 6CoronavirusInflammationSystemic inflammation2019-20 coronavirus outbreakImmunologyMeta-analysisVirologyInternal medicineDiseaseCytokine

Abstract

fetched live from OpenAlex

On March 11, 2020, the World Health Organization declared the outbreak of the novel SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) as a global pandemic. At the center of SARS-CoV-2 is the activation of inflammatory markers; remarkably, interleukin 6 and C-reactive protein seem to be consistently elevated in patients with SARS-CoV-2. Here, we showed that increased systemic C-reactive protein and interleukin 6 are common biomarkers of both severe COVID-19 and DSM-5-defined disorders. However, it is not known whether patients with psychiatric disorders with preexisting increased interleukin 6 and C-reactive protein are more vulnerable to severe complications of COVID-19 because of the additive inflammatory processes.

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.

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)0.004
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.007
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0110.005
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 head0.375
GPT teacher head0.461
Teacher spread0.086 · 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