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Record W2104086696 · doi:10.1309/ajcph27zizejlorf

Array Comparative Genomic Hybridization of Peripheral Blood Granulocytes of Patients With Myelodysplastic Syndrome Detects Karyotypic Abnormalities

2010· article· en· W2104086696 on OpenAlexafffund
Suzanne Vercauteren, Sandy Sung, Daniel T. Starczynowski, Wan L. Lam, Hélène Bruyèrè, Douglas E. Horsman, Peter Tsang, Heather A. Leitch, Aly Karsan

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2010
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsSt. Paul's HospitalUniversity of British ColumbiaVancouver General HospitalBC Cancer Agency
FundersCanadian Institutes of Health Research
KeywordsComparative genomic hybridizationKaryotypeBone marrowBiologyPathologyCD34Myelodysplastic syndromesCytogeneticsPeripheral bloodImmunologyChromosomeStem cellGeneticsMedicineGene

Abstract

fetched live from OpenAlex

The diagnosis of myelodysplastic syndromes (MDSs) relies largely on morphologic and karyotypic abnormalities, present in about 50% of patients with MDS. Array-based genomic platforms have identified copy number alterations in 50% to 70% of bone marrow samples of patients with MDS with a normal karyotype, suggesting a diagnostic role for these platforms. We investigated whether blood granulocytes harbor the same copy number alterations as the marrow of affected patients. Of 11 patients, 4 had cytogenetic abnormalities shown by conventional karyotyping involving chromosomes 5, 8, 11, 20, and X, and these changes were seen in the granulocytes of all 4 patients by using array comparative genomic hybridization (aCGH). Cryptic alterations were identified at a significantly higher level in marrow CD34+ cells compared with granulocytes (P < .0001). These data suggest that aCGH analysis of circulating granulocytes may be useful in detecting gross karyotypic alterations in patients with MDS when marrow examination has failed or not been done.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
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 head0.019
GPT teacher head0.325
Teacher spread0.306 · 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

Citations16
Published2010
Admission routes2
Has abstractyes

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