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Record W2735102775 · doi:10.1183/13993003.00953-2017

Computer-aided reading of tuberculosis chest radiography: moving the research agenda forward to inform policy

2017· editorial· en· W2735102775 on OpenAlexaff
Faiz Ahmad Khan, Tripti Pande, Belay Tessema, Rinn Song, Andrea Benedetti, Madhukar Pai, Knut Lönnroth, Claudia M. Denkinger

Bibliographic record

VenueEuropean Respiratory Journal · 2017
Typeeditorial
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Institute of Child Health and Human DevelopmentNational Institutes of HealthWorld Health OrganizationEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUnited States Agency for International Development
KeywordsMedicineReading (process)TuberculosisLimit (mathematics)Key (lock)RadiographyMedical physicsRadiologyPathologyLinguisticsComputer security

Abstract

fetched live from OpenAlex

Key gaps limit the evidence base for computer-aided reading of TB on CXR.We describe a research agenda to fill them http://ow.

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)0.028
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.028
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.122
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0040.002
Science and technology studies0.0020.003
Scholarly communication0.0100.011
Open science0.0050.002
Research integrity0.0230.031
Insufficient payload (model declined to judge)0.0090.005

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 head0.097
GPT teacher head0.412
Teacher spread0.315 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations60
Published2017
Admission routes1
Has abstractyes

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