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Record W2751910294 · doi:10.1002/jmv.24930

Influenza in patients with hematological malignancies: Experience at two comprehensive cancer centers

2017· article· en· W2751910294 on OpenAlexaff
Diana Vilar‐Compte, Dimpy P. Shah, Jakapat Vanichanan, Patricia Cornejo‐Juárez, Alejandro Garcia‐Horton, Patricia Volkow, Roy F. Chemaly

Bibliographic record

VenueJournal of Medical Virology · 2017
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsWestern University
FundersNational Cancer InstituteNational Institutes of Health
KeywordsMedicineInternal medicineCreatinineOdds ratioConfidence intervalCancerRetrospective cohort studyGastroenterology

Abstract

fetched live from OpenAlex

The burden of influenza infections in patients with hematological malignancies (HMs) is not well defined. We describe the clinical presentation and associated outcomes of influenza at two comprehensive cancer centers (center 1 in the United States and center 2 in Mexico). Clinical and laboratory data on patients with HMs and influenza infection diagnosed from April 2009 to May 2014 at the two centers were reviewed retrospectively. A total of 190 patients were included, the majority were male (63%) with a median age of 49 years (range, 1-88 years), and had active or refractory HMs (76%). Compared to center 1, patients in center 2 were significantly sicker (active cancer, decreased albumin levels, elevated creatinine levels, or hypoxia at influenza diagnosis) and experienced higher lower respiratory tract infection (LRI) rate (42% vs 7%; P < 0.001). In multivariable logistic regression analysis (odds ratio, 95% confidence interval), leukemia, (3.09, 1.23-7.70), decreased albumin level (3.78, 1.55-9.20), hypoxia at diagnosis (14.98, 3.30-67.90), respiratory co-infection (5.87, 1.65-20.86), and corticosteroid use (2.71, 1.03-7.15) were significantly associated with LRI; and elevated creatinine level (3.33, 1.05-10.56), hypoxia at diagnosis (5.87, 1.12-30.77), and respiratory co-infection (6.30, 1.55-25.67) were significantly associated with 60 day mortality in both centers. HM patients with influenza are at high risk for serious complications such as LRI and death, especially if they are immunosuppressed. Patients with respiratory symptoms should seek prompt medical care during influenza season.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.075
GPT teacher head0.433
Teacher spread0.357 · 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

Citations38
Published2017
Admission routes1
Has abstractyes

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