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Record W2468868796 · doi:10.1097/ipc.0000000000000418

Co-Recovery of Mycobacterium avium Complex and Mycobacterium tuberculosis in Patients With Pulmonary Tuberculosis

2016· article· en· W2468868796 on OpenAlexaff
M. Christina Dogbey-Smith, David Schlossberg

Bibliographic record

VenueInfectious Diseases in Clinical Practice · 2016
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsMedicineSputumTuberculosisMycobacterium tuberculosisDiscontinuationMycobacterium avium complexInternal medicineDiseaseIncidence (geometry)Sputum cultureAcid-fastMycobacteriumPredictive valuePulmonary tuberculosisPathology

Abstract

fetched live from OpenAlex

Background Patients eventually shown to have pulmonary tuberculosis often grow both Mycobacterium avium complex (MAC) and Mycobacterium tuberculosis (MTB) from sputum. If the MAC grows first, patients may erroneously be assumed to have MAC disease, with discontinuation of isolation, contact investigation and alterations in chemotherapy. In patient with compatible disease, it would be helpful to know the likelihood of recovering both MAC and MTB as well as the predictive value of a positive sputum smear. Methods During 2012 and 2013 the Tuberculosis Control Program of the Philadelphia Department of Public health evaluated the relative incidence of MAC versus MTB in patients with suspected pulmonary tuberculosis, the time to culture growth from the initial smear for MTB and MAC, and determined the positive predictive value of a positive acid fast smear. Results In this setting, 22% of patients that were culture positive for MTB also grew MAC, with 78% of them growing MAC before MTB. The positive predictive value of a positive acid fast smear was 89% for MTB versus MAC. Conclusions There is a relatively high frequency of recovering MAC in TB patients Therefore, the recovery of MAC in a patient with a positive acid fast smear and compatible disease should not rule out MTB until cultures are final.

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)0.001
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.348
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 teacher head, not a consensus.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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