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Record W2101743166 · doi:10.1186/1471-2334-12-218

High yield of culture-based diagnosis in a TB-endemic setting

2012· article· en· W2101743166 on OpenAlexaff
Anne‐Marie Demers, Suzanne Verver, Andrew Boulle, Robin M. Warren, Paul D. van Helden, Marcel A. Behr, David Coetzee

Bibliographic record

VenueBMC Infectious Diseases · 2012
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University Health CentreUniversité de MontréalMcGill University
Fundersnot available
KeywordsMedicineMedical microbiologyTuberculosisGold standard (test)Yield (engineering)Tropical medicineIncidence (geometry)PathologyInternal medicineVirologyMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: In most of the world, microbiologic diagnosis of tuberculosis (TB) is limited to microscopy. Recent guidelines recommend culture-based diagnosis where feasible. METHODS: In order to evaluate the relative and absolute incremental diagnostic yield of culture-based diagnosis in a high-incidence community in Cape Town, South Africa, subjects evaluated for suspected TB had their samples processed for microscopy and culture over a 21 month period. RESULTS: For 2537 suspect episodes with 2 smears and 2 cultures done, 20.0% (508) had at least one positive smear and 29.9% (760) had at least one positive culture. One culture yielded 1.8 times more cases as 1 smear (relative yield), or an increase of 12.0% (absolute yield). Based on the latter value, the number of cultures needed to diagnose (NND) one extra case of TB was 8, compared to 19 if second specimens were submitted for microscopy. CONCLUSION: In a high-burden setting, the introduction of culture can markedly increase TB diagnosis over microscopy. The concept of number needed to diagnose can help in comparing incremental yield of diagnosis methods. Although new promising diagnostic molecular methods are being implemented, TB culture is still the gold standard.

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)0.000
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.323
Teacher spread0.295 · 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.

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

Citations16
Published2012
Admission routes1
Has abstractyes

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