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Record W1967128028 · doi:10.1371/journal.pmed.0040050

XDR-TB in South Africa: No Time for Denial or Complacency

2007· article· en· W1967128028 on OpenAlexafffund
Jerome Amir Singh, Ross Upshur, Nesri Padayatchi

Bibliographic record

VenuePLoS Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthBill and Melinda Gates FoundationUniversity of TorontoU.S. Department of Health and Human Services
KeywordsTuberculosisMedicineCase fatality rateSputumRifampicinMycobacterium tuberculosisHuman immunodeficiency virus (HIV)DemographyPediatricsEnvironmental healthVirologyPopulationPathology

Abstract

fetched live from OpenAlex

On September 1, 2006, the World Health Organisation (WHO) announced that a deadly new strain of extensively drug-resistant tuberculosis (XDR-TB) had been detected in Tugela Ferry (Figure 1), a rural town in the South African province of KwaZulu-Natal (KZN) [1], the epicentre of South Africa’s HIV/AIDS epidemic. Of the 544 patients studied in the area in 2005, 221 had multi-drug-resistant tuberculosis (MDR-TB), that is, Mycobacterium tuberculosis that is resistant to at least rifampicin and isoniazid. Of these 221 cases, 53 were identified as XDR-TB (see Table 1 and [2]), i.e., MDR-TB plus resistance to at least three of the six classes of second-line agents [3]. This reportedly represents almost
\none-sixth of all known XDR-TB cases reported worldwide [4]. Of the 53, 44 were tested for HIV and all were HIV infected. The median survival from the time of sputum specimen collection was 16 days for 52 of the 53 infected individuals, including six health workers and those reportedly taking antiretrovirals [2]. Such a fatality rate for XDR-TB, especially within such a relatively short period of time, is unprecedented
\nanywhere in the world.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.001

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 head0.087
GPT teacher head0.365
Teacher spread0.278 · 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
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

Citations183
Published2007
Admission routes2
Has abstractyes

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