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Record W2407217237 · doi:10.1111/his.12999

Issues in diagnosis of small B cell lymphoid neoplasms involving the bone marrow and peripheral blood. Report on the Bone Marrow Workshop of the XVIIth meeting of the European Association for Haematopathology and the Society for Hematopathology

2016· article· en· W2407217237 on OpenAlexaff
Anna Porwit, Falko Fend, Marcus Kremer, Attilio Orazi, Mükerrem Safalı, Jon van der Walt

Bibliographic record

VenueHistopathology · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsBone marrowImmunophenotypingMedicinePathologyLymphoproliferative disordersB cellLymphomaImmunologyFlow cytometryAntibody

Abstract

fetched live from OpenAlex

Small B cell lymphoid neoplasms are the most common lymphoproliferative disorders involving peripheral blood (PB) and bone marrow (BM). The Bone Marrow Workshop (BMW) organized by the European Bone Marrow Working Group (EBMWG) of the European Association for Haematopathology (EAHP) during the XVIIth EAHP Meeting in Istanbul, October 2014, was dedicated to discussion of cases illustrating how the recent advances in immunophenotyping, molecular techniques and cytogenetics provide better understanding and classification of these entities. Submitted cases were grouped into following categories: (i) cases illustrating diagnostic difficulties in chronic lymphocytic leukaemia (CLL); (ii) cases of BM manifestations of small B cell lymphoid neoplasms other than CLL; (iii) transformation of small B cell lymphoid neoplasms in the BM; and (iv) multiclonality and composite lymphomas in the BM. This report summarizes presented cases and conclusions of the BMW and provides practical recommendations for classification of the BM manifestations of small B cell lymphoid neoplasms based on the current state of knowledge.

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)0.009
metaresearch head score (Gemma)0.006
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 score0.272
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.252
Teacher spread0.235 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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