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Record W4281769948 · doi:10.1111/ijlh.13901

Thrombolysis in the recovery of coagulated bone marrow aspirate samples

2022· letter· en· W4281769948 on OpenAlexaff
Bogdan Mihai Beca, M Radford, Benjamin D. Hedley, Ian Chin‐Yee, Alejandro Lazo‐Langner, Cyrus C. Hsia

Bibliographic record

VenueInternational Journal of Laboratory Hematology · 2022
Typeletter
Languageen
FieldMedicine
TopicHematological disorders and diagnostics
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsFlow cytometryBone marrow aspirateBone marrowStainGiemsa stainSample (material)PathologyMedicineBiomedical engineeringMolecular biologyChromatographyChemistryBiologyStainingImmunology

Abstract

fetched live from OpenAlex

Supplementary Figure 1 Flow Cytometry Output Example on TPA Marrow Aspriate Sample Flow cytometry output graphs of a representative TPA marrow aspirate sample. Supplementary Figure 2 Example of Non-TPA vs TPA Marrow Aspirate Sample Morphology on the Same Patient Panel A non-TPA sample, light microscopy at 100x, inset 500x, Wright-Giemsa stain Panel B TPA sample, light microscopy at 100x, inset 500x, Wright-Giemsa stain Supplementary Table 1 Flow Cytometry Fluorochrome-Conjugated Antibody Selection Panels Fluorochrome-conjugated antibodies used in two panels for this study. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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 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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.009

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 head0.031
GPT teacher head0.302
Teacher spread0.272 · 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 designBench or experimental
Domainnot available
GenreCommentary

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

Citations0
Published2022
Admission routes1
Has abstractyes

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