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Record W3175708847 · doi:10.1111/tid.13679

Treatment outcomes of nontuberculous mycobacterial pulmonary disease in lung transplant recipients

2021· article· en· W3175708847 on OpenAlexaff
Takashi Hirama, L.G. Singer, Sarah K. Brode, Theodore K. Marras, Shahid Husain

Bibliographic record

VenueTransplant Infectious Disease · 2021
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsWest Park Healthcare CentreToronto General HospitalUniversity of TorontoUniversity Health Network
FundersTakeda Science Foundation
KeywordsMedicineNontuberculous mycobacteriaCulture conversionInternal medicineRetrospective cohort studyLung transplantationRadiological weaponLungSurgeryTuberculosisMycobacteriumSputumPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Lung transplant (LTX) recipients are at risk miscellaneous infections, among whom the clinical significance of nontuberculous mycobacteria (NTM) is increasingly recognized. Despite anti-mycobacterial therapy becoming standardized worldwide, there is a lack of data on treatment outcomes in LTX recipients who develop NTM-pulmonary disease (PD). We aimed to review the treatment outcomes of NTM-PD among LTX recipients in our center. METHODS: Patients who underwent LTX from January 2013 to December 2014 were consecutively enrolled in the retrospective cohort, with follow-up of data retrieved to December 2017. Clinical and radiological improvement and culture conversion after anti-mycobacterial therapy were reviewed in those who developed post-transplant NTM-PD. RESULTS: Sixteen of 230 LTX recipients developed post-transplant NTM-PD. Ten of 16 patients with post-transplant NTM-PD were treated with macrolide-containing anti-mycobacterial therapy, leading to clinical improvement in 5/10 (50%), radiological improvement in 5/10 (50%) and culture conversion in 6/10 (60%) patients. CONCLUSION: Anti-mycobacterial therapy may relieve pulmonary symptoms and reduce microbial load among individuals with post-transplant NTM-PD.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.275
Teacher spread0.261 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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