MétaCan
Menu
Back to cohort
Record W2748740204 · doi:10.1093/ofid/ofx163.1801

Rate of All-cause Hospitalization at Year 2 Between Treatment Groups Following Diagnosis of Nontuberculous Mycobacterial Lung Disease in the USA

2017· article· en· W2748740204 on OpenAlexaff
Theodore K. Marras, Mehdi Mirsaeidi, Engels Chou, Gina Eagle, Raymond Zhang, Ping Wang, Quanwu Zhang

Bibliographic record

VenueOpen Forum Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineNontuberculous mycobacteriaInternal medicineComorbidityEthambutolTuberculosisBronchiectasisLogistic regressionLung fibrosisAsthmaRifampicinLungPediatricsGastroenterologyPulmonary fibrosisMycobacteriumPathology

Abstract

fetched live from OpenAlex

The study compared rates of hospitalization between treatment groups in patients with nontuberculous mycobacterial lung disease (NTMLD) in a US national managed care claims database. Patient (N = 1039) pharmacy claims at year 1 following NTMLD diagnosis were classified into 3 treatment groups including triple combo (macrolide + ethambutol + rifamycin ± other drugs) (G1), other antibiotics used by physicians for NTMLD (G2), and no treatment (G3). Hospitalization rates at year 2 were compared between treatment groups using mixed effects logistic regression to adjust for patient characteristics and comorbidities measured by Charlson Comorbidity Index (CCI) during the 12 months prior to NTMLD diagnosis (baseline). Mean age was 66, 66 and 73 years with 65%, 70% and 66% women in G1 (n = 353), G2 (n = 388) and G3 (n = 298) respectively. At baseline, there was no difference on CCI (CCI≈2) between treatment groups. However, comorbidity distribution differed prominently in asthma (22.1%, 26.3% and 11.4%), arrhythmia (19.3%, 19.3% and 27.2%), cystic fibrosis (0.8%, 4.6% and 0%), immune disorder (7.6%, 9% and 2.7%), pneumonia (49.0%, 41.8% and 32.6%), and tuberculosis (9.3%, 8.2% and 5.4%), and in immunosuppressant use (51%, 51.5% and 25.2%). Baseline hospitalization was 31.7% in G1, 33.0% in G2, and 25.8% in G3. At year 2, CCI stayed almost unchanged from the baseline scores (1.9 in G1, 2.0 in G2, and 1.9 in G3). Unadjusted hospitalization rates were 19.6%, 27.8% vs 20.8%, and adjusted rates were 44.5%, 56.1% and 47.8% in 3 groups respectively (Figure). G2 had a 60% increase in risk of hospitalization after adjustment (odds ratio (OR)=1.60, 95% CI: 1.11–2.29, P = 0.01) compared with G1 but no statistically significant difference compared with G3 (OR=1.40, P = 0.08). Cerebrovascular disease (OR=1.8, P < 0.02), COPD (OR=1.60, P < 0.01), cystic fibrosis (OR=5.85, P < 0.01), depression (OR=1.64, P < 0.05), and other lung disease (OR=1.42, P < 0.05) were associated with a higher risk of hospitalization at year 2 after NTMLD diagnosis. We observed a lower hospitalization rate in NTMLD patients receiving antibiotics that were concordant with first line ATS/IDSA guidelines recommendations in comparison with those who used other antibiotic regimens. E. Chou, Insmed Incorporated: Employee, Salary; G. Eagle, Insmed Incorporated: Employee, Salary; R. Zhang, Insmed Incorporated: Consultant, Consulting fee; P. Wang, Insmed Incorporated: Employee, Salary; Q. Zhang, Insmed Incorporated: Employee, Salary

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)0.003
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.347
Teacher spread0.314 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueOpen Forum Infectious DiseasesSame topicMycobacterium research and diagnosisFrench-language works237,207