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Record W4225399187 · doi:10.3390/curroncol29050264

Invasive Fungal Disease in Patients with Chronic Lymphocytic Leukemia in Japan: A Retrospective Database Study

2022· article· en· W4225399187 on OpenAlexvenueno aff
Takeo Yasu, Kotono Sakurai, Manabu Akazawa

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChronic lymphocytic leukemiaRetrospective cohort studyDiseaseLeukemiaDatabasePathologyImmunology

Abstract

fetched live from OpenAlex

Invasive fungal disease (IFD) is an important cause of morbidity and mortality in patients with hematological malignancies. As chronic lymphocytic leukemia (CLL) is a rare hematological malignancy in Japan, IFD incidence in Japanese patients with CLL is unclear. This study aimed to investigate IFD incidence in Japanese patients with CLL. This retrospective cohort study used data of patients with CLL registered between April 2008 and December 2019 in the Medical Data Vision database (n = 3484). IFD incidence after CLL diagnosis in the watch-and-wait (WW) and drug therapy (DT) groups was 1.5% and 9.2%, respectively. The most common type of IFD was invasive aspergillosis (28.1%). Cox proportional hazards multivariate analysis revealed that DT (hazard ratio [HR]: 2.13) and steroid use (HR: 4.19) were significantly associated with IFD occurrence. IFD incidence was significantly higher in the DT group than in the WW group (log-rank p < 0.001); however, there was no significant between-group difference in the time to IFD onset or the type of IFD (p = 0.09). This study determined the incidence of IFD in patients with CLL during WW. Physicians should monitor for IFD, even among patients with CLL undergoing the WW protocol.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.043
GPT teacher head0.357
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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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