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Distinct characteristics and new prognostic scoring system for Chinese patients with Waldenström macroglobulinemia

2014· article· en· W2405133210 on OpenAlexaff
Shuhua Yi, Rui Cui, Zengjun Li, Gang An, Junyuan Qi, Dehui Zou, Peihong Zhang, Hui-shu Chen, Jianxiang Wang, Hong Chang, Lugui Qiu

Bibliographic record

VenueChinese Medical Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineWaldenstrom macroglobulinemiaInternal medicineMacroglobulinemiaUnivariate analysisMultivariate analysisInternational Prognostic Scoring SystemMalignancyAsymptomaticGastroenterologyMultiple myelomaLymphomaMyelodysplastic syndromesBone marrow

Abstract

fetched live from OpenAlex

BACKGROUND: Waldenström macroglobulinemia (WM) is an uncommon lymphoid malignancy. The characteristics and prognosis of WM have never been systematically studied in the East. METHODS: We analyzed the clinical characteristics and the prognostic factors of 90 Chinese WM patients, and compared them with the Western reports. RESULTS: The median age was 62 years old with a male-to-female ratio of 3.74. The most common symptoms at diagnosis were fatigue (77.8%) and bleeding (20%), while only 6 patients (6.7%) were asymptomatic. In the univariate analysis, age >62 years, thrombocytopenia, leucopenia, cytopenias ≥ 2, and high risk on the international prognostic scoring system for WM were the adverse risk factors, but only age >62 years and ≥ 2 cytopenias were the independent prognostic factors in the multivariate analysis. Using age <62 years and ≥ 2 cytopenias, three significantly different prognostic groups could been distinguished, with 5-year overall survival of 71.6%, 48.6%, and 17.0% (P < 0.001). CONCLUSION: Distinct characteristics exist in WM in China compared to the West and we describe a new simple prognostic model for newly diagnosed WM patients.

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)0.008
Version: codex-gemma-dda1882f352aValidation 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 score0.076
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.275
Teacher spread0.266 · 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.

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

Citations8
Published2014
Admission routes1
Has abstractyes

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