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Record W4396773014 · doi:10.4081/aiua.2024.12449

Does intravesical BCG for bladder cancer protect from COVID-19?

2024· article· en· W4396773014 on OpenAlexaffabout
Anastasia MacDonald, Lexia Bao, Vahid Mehrnoush, Asmaa Ismail, Livio Di Matteo, Ahmed S. Zakaria, Hazem Elmansy, Peter C. Black, Ahmed Kotb

Bibliographic record

VenueArchivio Italiano di Urologia e Andrologia · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune responses and vaccinations
Canadian institutionsUniversity of British Columbia HospitalLakehead UniversityUniversity of British ColumbiaNOSM University
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Bladder cancerSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakUrologyOncologyCancerVirologyInternal medicineInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

INTRODUCTION: The study aimed to correlate the history of intravesical BCG as well as infantile BCG immunization with the incidence and severity of COVID-19 infection. METHODS: Retrospective data collection of patients with high-risk non muscle invasive bladder cancer (NMIBC) from two Canadian centers. Data collection included a history of BCG instillation, infantile immunization, and the development of COVID-19 infection. Admission and/ or mortality because of COVID-19 was reported. RESULTS: We could include data from 348 patients: including 188 and 160 patients from Ontario and British Columbia respectively. COVID-19 affected 15% of these patients. Intravesical BCG was used in 44% of these patients. Intravesical BCG and/or infantile BCG immunization did not correlate with the incidence of COVID-19 infection. CONCLUSIONS: Previous intravesical BCG and/ or a history of infantile BCG vaccination were not more/ less frequent in patients who had COVID-19 infection.

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)0.003
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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.303
Teacher spread0.281 · 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
Published2024
Admission routes2
Has abstractyes

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