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Record W2013169247 · doi:10.1038/leu.2014.191

Revisiting guidelines for integration of flow cytometry results in the WHO classification of myelodysplastic syndromes—proposal from the International/European LeukemiaNet Working Group for Flow Cytometry in MDS

2014· article· en· W2013169247 on OpenAlexaff
Anna Porwit, Arjan A. van de Loosdrecht, Peter Bettelheim, Lisa Eidenschink Brodersen, Kate Burbury, Eline M.P. Cremers, Matteo Giovanni Della Porta, Robin Ireland, Ulrika Johansson, Sergio Matarraz, Kíyoyuki Ogata, Frank Preijers, Katherina Psarra, Dolores Subirá, Peter Valent, Vincent H. J. van der Velden, D. Wells, Theresia M. Westers, Wolfgang Kern, Marie C. Béné

Bibliographic record

VenueLeukemia · 2014
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMyelodysplastic syndromesMedicineWork flowFlow cytometryMyeloidWorking groupMedical physicsFamily medicinePathologyInternal medicineImmunologyBone marrowComputer science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.118
metaresearch head score (Gemma)0.141
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.118
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1180.141
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0110.008
Science and technology studies0.0030.010
Scholarly communication0.0110.005
Open science0.0130.006
Research integrity0.0140.025
Insufficient payload (model declined to judge)0.0020.003

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 head0.079
GPT teacher head0.348
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations145
Published2014
Admission routes1
Has abstractno

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