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Record W4323875200 · doi:10.12688/f1000research.3-4.v2

Two fatal cases of melioidosis on the Thai-Myanmar border

2014· preprint· en· W4323875200 on OpenAlexaff
Cindy S. Chu, Stuart Winearls, Clare Ling, Miriam B. Torchinsky, Aung Pyae Phyo, Warat Haohankunnathum, Paul Turner, Vanaporn Wuthiekanun, François Nosten

Bibliographic record

VenueF1000Research · 2014
Typepreprint
Languageen
FieldMedicine
TopicBurkholderia infections and melioidosis
Canadian institutionsUniversity of British Columbia
FundersCenters for Disease Control and PreventionWellcome Trust
KeywordsMelioidosisBurkholderia pseudomalleiSoutheast asiaOpen peer reviewMedicineDiseasePublic healthEnvironmental healthBiologyPathologyAncient historyPlant biologyHistory

Abstract

fetched live from OpenAlex

Melioidosis is endemic in areas of Southeast Asia, however, there are no published reports from the Thai-Myanmar border. We report the first two documented cases of fatal melioidosis in this region. This is of great public health importance and highlights the need to both increase clinical awareness of melioidosis on the Thai-Myanmar border, and to assess the true burden of disease in the area through improved case detection and Burkholderia pseudomallei prevalence studies.

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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.084
GPT teacher head0.410
Teacher spread0.326 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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