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Record W3035728590 · doi:10.1016/j.mmcr.2020.06.003

“False negative” CSF cryptococcal antigen with clinical meningitis: Case reports and review of literature

2020· article· en· W3035728590 on OpenAlexfundno aff
Morris K Rutakingirwa, Tadeo K Kiiza, Joshua Rhein

Bibliographic record

VenueMedical Mycology Case Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsnot available
FundersFogarty International CenterNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institute of Allergy and Infectious DiseasesGrand Challenges CanadaWellcome Trust
KeywordsCryptococcal meningitisMedicineImmunosuppressionImmunologyMeningitisCryptococcusAntigenPediatricsHuman immunodeficiency virus (HIV)MicrobiologyBiologyViral disease

Abstract

fetched live from OpenAlex

There is an increasing recognition of patients presenting with cryptococcal meningitis despite having a negative CSF cryptococcal antigen (CrAg). In this report, we describe three cases of patients with advanced immunosuppression who presented to hospital with "false negative" CSF cryptococcal antigen, two of whom had a positive fungal culture. We describe the challenge of CSF-CrAg negative cryptococcal meningitis and explore ways to overcome this challenge using newer diagnostic techniques.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.001
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 head0.028
GPT teacher head0.350
Teacher spread0.322 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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