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Record W4293569195 · doi:10.1186/s13223-022-00715-w

Seronegative immunity to SARS-CoV-2: a case study

2022· article· en· W4293569195 on OpenAlexvenueno aff
Kyle D Miller, Amie M. Ashcraft, Courtney Pilkerton, Carl D. Shrader

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersNational Institute of General Medical Sciences
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Virology2019-20 coronavirus outbreakImmunityMedicineBiologyImmunologyImmune systemOutbreakInfectious disease (medical specialty)Internal medicineDisease

Abstract

fetched live from OpenAlex

BACKGROUND: COVID-19 presents with a variable clinical course from asymptomatic to severe respiratory distress with nearly 25% mortality in mechanically-ventilated patients. As such, there is uncertainty regarding how host factors modulate the disease course. CASE PRESENTATION: This report examines these factors in two geriatric patients with multiple comorbid conditions who were residents of the long-term care facility in West Virginia that was the epicenter of COVID-19 in the state. Each patient had substantial, unprotected exposure to SARS-CoV-2 with subsequent negative PCR and antibody testing. CONCLUSIONS: These cases could represent an important step in understanding host factors that modulate the disease course and susceptibility of patients exposed to SARS-CoV-2, and illustrate the need for further research into host resistance relating to this pandemic.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.084
GPT teacher head0.425
Teacher spread0.340 · 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 designCase report
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
Published2022
Admission routes1
Has abstractyes

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