MétaCan
Menu
Back to cohort
Record W2013585608 · doi:10.1183/09031936.00106914

Using cerebrospinal fluid for the diagnosis of tuberculous meningitis with GeneXpert

2014· letter· en· W2013585608 on OpenAlexaff
Claudia M. Denkinger, Madhukar Pai

Bibliographic record

VenueEuropean Respiratory Journal · 2014
Typeletter
Languageen
FieldMedicine
TopicInfectious Diseases and Tuberculosis
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersSchool of Medicine, Stanford University
KeywordsGeneXpert MTB/RIFMedicineCerebrospinal fluidTuberculous meningitisTuberculosisMeningitisFirst lineSecond lineMycobacterium tuberculosisIntensive care medicineInternal medicinePathologySurgery

Abstract

fetched live from OpenAlex

concentration. However, four replicate samples using the same protocol also demonstrated a 1.7 cycle mean difference, similar to the up to 1.9 cycle difference seen across protocols. The similarity in cycle differences suggests that differences in cycle threshold values may be due to intra-assay variation and not related to the sample processing protocol used. These results show that centrifugation may not be necessary for testing CSF. Additionally, as CSF samples tend to be paucibacillary compared to sputum, less sample reagent may be needed to produce an adequate tuberculocidal effect. Further research, ideally on paired samples from TB meningitis patients, will help elucidate the most sensitive and safest method to test CSF with the Xpert MTB/RIF test, particularly in settings that may not have access to centrifugation.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.592
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.301
Teacher spread0.235 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Respiratory JournalSame topicInfectious Diseases and TuberculosisFrench-language works237,207