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Record W2805733040 · doi:10.1136/bmj.k2345

Nine in 10 children who die from TB went untreated

2018· article· en· W2805733040 on OpenAlexaboutno aff
Jacqui Thornton

Bibliographic record

VenueBMJ · 2018
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsSick childTuberculosisLung diseaseQuarter (Canadian coin)PediatricsMedicineSoviet unionFamily medicineDemographyHistoryPolitical scienceLawLungSociology

Abstract

fetched live from OpenAlex

Javier Galeano/The Union Five year old Ugandan boy Isaac (pictured) is one of the estimated one million children under the age of 15 years who become sick with tuberculosis every year, nearly a quarter of whom (239 000) die. Luckily for him, Isaac was one of the minority of children who were treated. Of the children who die from the disease, nine in 10 had not been treated. Most cases, a new report argues,1 are preventable, treatable, and curable with simple, cost effective public health measures. On 23 May the organisation behind the report, the International Union against Tuberculosis and Lung Disease, launched a call to action …

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.003

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 head0.030
GPT teacher head0.365
Teacher spread0.334 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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