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Record W2122157012

The tuberculin skin test: a comparison of ruler and calliper readings.

2010· article· en· W2122157012 on OpenAlexaff
Hennie Geldenhuys, Suzanne Verver, Shireen Surtie, Mark Hatherill, Frank van Leth, Fazlin Kafaar, Michèle Tameris, W. Kleynhans, Angelique Kany Kany Luabeya, Sizulu Moyo, Welile Sikhondze, Willem A. Hanekom, Hassan Mahomed

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineTuberculinCalipersMantoux testKappaRulerTuberculosisSkin testLimits of agreementTuberculin testDermatologyNuclear medicinePathologyMathematics
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The Mantoux tuberculin skin test (TST) is widely used to diagnose latent infection with Mycobacterium tuberculosis. TST skin induration may be measured either by a transparent ruler or by a pair of callipers. We hypothesised that the type of instrument used may affect the reading. OBJECTIVE: To determine whether variability in Mantoux TST measurement is affected by the type of reading instrument. METHOD: A TST (Mantoux method) was performed among healthy adolescents. The indurations were read with among ruler and calliper by two independent readers. Limits of agreement and Kappa (κ) scores at TST positivity cut-off points were calculated. A Bland-Altman graph was constructed. RESULTS: The 95% limits of agreement between instruments ranged from -5 mm to 3 mm. The limits of agreement between readers ranged from -5 mm to 4 mm. κ scores between instruments were respectively 0.7 and 0.8 at 15 mm and 10 mm cut-offs. CONCLUSION: The variability between readers of TST indurations is not influenced by changing the reading instrument.

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)0.011
metaresearch head score (Gemma)0.041
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 score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.030
GPT teacher head0.320
Teacher spread0.290 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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Same venuePubMed→Same topicTuberculosis Research and Epidemiology→French-language works237,207→