MétaCan
Menu
Back to cohort
Record W2898710294 · doi:10.1093/ofid/ofy209.013

122. All-Cause Mortality Increased With Nontuberculous Mycobacterial Lung Disease in US Medicare

2018· article· en· W2898710294 on OpenAlexaff
Theodore K. Marras, Quanwu Zhang, Mehdi Mirsaeidi, Christopher Vinnard, Keith Hamilton, Jennifer Adjemian, Gina Eagle, Ping Wang, Raymond Zhang, Engels Chou, Kevin Winthrop

Bibliographic record

VenueOpen Forum Infectious Diseases · 2018
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCohortInternal medicinePoisson regressionComorbidityProportional hazards modelMortality rateCohort studyPopulation

Abstract

fetched live from OpenAlex

Abstract Background Nontuberculous Mycobacterial Lung Disease (NTMLD) is a chronic, debilitating, and progressive disease. This study evaluates all-cause mortality in patients with NTMLD in the US Medicare. Methods Patients (n = 43,394) were identified from the Medicare database (excluding Part C) based on physician claims for NTMLD on ≥2 separate occasions ≥30 days apart between 2007 and 2015. About 12% patients were <65 years and qualified for Medicare due to disability. A control cohort (n = 84,814) was randomly selected and matched to the NTMLD sample by age and sex. The NTMLD diagnosis date was assigned to the matched controls as an index date. Poisson and Cox regression were used to derive descriptive rates and adjusted risk of mortality accounting for baseline comorbidities of pulmonary, immune, cardiovascular, cancer, and other disorders. Results Mean age was 74 (±10) years and 68% were female in both NTMLD and control cohorts. Mean Charlson comorbidity index (CCI) was 2.9 (standard deviation ±2.6) in NTMLD vs. 1.3 (±1.9) in control cohort. In Medicare members ≥65 years, mean age was 76 (±7) years and 70% were female. Mean CCI was 2.8 (±2.5) in NTMLD cohort vs. 1.4 (±2.0) in control cohort. In Medicare members <65, mean age was 53 (±10) and 49% were female. Mean CCI was 3.8 (±3.3) in NTMLD vs. 1.1 (±1.9) in the control. Observed yearly mortality rates were 9.8% in NTMLD vs. 4.7% in control cohort (rate ratio [RR] = 2.1; 95% CI: 2.03–2.13). In ≥65 Medicare members, the observed rates were 9.7% in NTMLD vs. 5.0% in control cohort (RR = 2.0; 1.9–2.0). In Medicare members <65, the observed rates were 10.4% in NTMLD vs. 2.5% in control cohort (RR = 4.1; 3.8–4.5). Compared with the Asian race, observed mortality was higher in NTMLD patients of Native American (hazard ratio [HR] = 1.69, 1.30–2.19), Black (HR = 1.23; 1.08–1.39), Hispanic (HR = 1.27, 1.07–1.51), or White (HR = 1.18, 1.06–1.31) race (Figure 1). Mortality rates were elevated with NTMLD relative to controls in all age categories from ≥65 years (Figure 2). Adjusted mortality increased with NTMLD by 35% overall (HR = 1.35; 1.3–1.4), by 23% in age group ≥65 (HR = 1.23, 1.19–1.27), and almost doubled in age group <65 (HR = 1.97, 1.80–2.15). Conclusion Among US Medicare enrollees, NTMLD was associated with a 35% increased risk of mortality overall. Disclosures T. Marras, Insmed Incorporated: Investigator, Consulting fee and Research grant. Horizon Pharmaceuticals: Consultant, Consulting fee. Red Hill: Consultant, Consulting fee. AstraZeneca: CME, Speaker honorarium. Q. Zhang, Insmed Incorporated: Employee, Salary. G. Eagle, Insmed Incorporated: Employee, Salary. P. Wang, Insmed Incorporated: Employee, Salary. R. Zhang, Insmed Incorporated: Consultant, Consulting fee. E. Chou, Insmed Incorporated: Employee, Salary. K. L. Winthrop, Insmed Incorporated: Consultant and Scientific Advisor, Consulting fee and Research grant.

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 score0.013
Threshold uncertainty score0.027

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.332
Teacher spread0.313 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

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