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Record W3009325993 · doi:10.14740/jocmr4058

MAC Attack: Clinical Correlates of <i>Mycobacterium avium</i> Complex Infection Among Patients With and Without Cancer

2020· article· en· W3009325993 on OpenAlexvenueno aff
Karan Gupta, Marc B. Feinstein, Debra A. Goldman, Hassan Patail, Diane E. Stover

Bibliographic record

VenueJournal of Clinical Medicine Research · 2020
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLung cancerCancerAntibioticsInternal medicineMycobacterium avium complexPneumoniaMycobacterium avium-intracellulare infectionTuberculosisPathologyMycobacterium

Abstract

fetched live from OpenAlex

BACKGROUND: complex (MAC), an increasingly common respiratory organism worldwide. Determining when this represents a true respiratory pathogen remains controversial and becomes increasingly challenging in patients with cancer. This study aims to 1) describe the phenotype that exists among cancer patients with MAC colonization and MAC pulmonary infection when compared to non-cancer patients; 2) assess whether cancer, symptoms, and radiographs, were associated with the decision to treat MAC pulmonary infection with antibiotics. METHODS: We retrospectively analyzed 550 adult, non-human immunodeficiency virus (HIV) patients, among whom MAC was identified in respiratory cultures or tissue. Radiographs, clinical symptoms and cancer status were studied. Patients were categorized as having MAC pulmonary infection based on 2007 ATS guidelines, and antibiotic treatment was thereafter reviewed. Fisher's exact test and Wilcoxon Rank sum assessed differences. RESULTS: Median age of the 550 patients was 68 years; most were female (56%) and white (83%). Symptoms and radiographic abnormalities accompanying MAC isolation were common, occurring among 83% and 99.6% respectively of all patients. There were 444 patients with MAC who had current or inactive cancers, most commonly hematologic (30%) and lung (25%) malignancies, while 106 patients never had cancer. Cancer patients were younger (P = 0.028), less often female (P < 0.001), and had less-frequent pre-existing lung disease (P = 0.017) than those without cancer. There were 196 (35%) patients determined to have MAC pulmonary infection, among whom 49 (9%) received directed antibiotics. Those receiving antibiotics had lower body mass index (BMI) (P < 0.0001), more frequent pre-existing lung disease (P = 0.003) and lower cancer rates (P = 0.008) than those not receiving antibiotics. Patients receiving antibiotics were more likely to have cavitary disease (P = 0.001), cough/dyspnea (P = 0.012), hemoptysis (P < 0.001), and constitutional symptoms (P = 0.001). CONCLUSIONS: In concordance with ATS guidelines, hemoptysis, constitutional symptoms, cough/dyspnea and cavitary disease were associated with highest likelihood to treat with antibiotics. The phenotype in cancer patients was quite different than the classic Lady Windermere syndrome. MAC pulmonary infection was treated less often in cancer patients. This study extends beyond the ATS guidelines to examine the potential import of malignancy on the colonization and potential treatment of MAC.

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)0.000
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.268
GPT teacher head0.526
Teacher spread0.258 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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