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The Utility of Bone Marrow Examinations for the Diagnosis of Immune Thrombocytopenia.

2010· article· en· W2979826979 on OpenAlexaff
Vishwanath Kishan Mahabir, Cathy A. Ross, Snežana Popović, Jacqueline M. Bourgeois, Grace Wang, James N. George, Wendy Lim, John G. Kelton, Donald M. Arnold

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsJuravinski HospitalMcMaster University
Fundersnot available
KeywordsMedicineBone marrowBiopsyBone marrow failurePathologyMyelofibrosisAplastic anemiaHaematopoiesisInternal medicine

Abstract

fetched live from OpenAlex

Abstract Abstract 3691 Background: The utility of bone marrow examinations for the investigation of patients with immune thrombocytopenia (ITP) is a matter of debate. We designed an agreement study to evaluate the inter-rater reliability of bone marrow biopsies and aspirates for the diagnosis of ITP. Methods: Histological slides of bone marrow biopsies from patients with ITP and controls were prepared from stored tissue blocks and corresponding aspirate slides were retrieved for blinded, duplicate, independent pathological review. Patients had a diagnosis of primary ITP, were 18 years of age or older with a platelet count less than 100 ×109/L as measured within 4 weeks before the date of bone marrow biopsy procurement. Patients with splenomegaly, myelodysplastic syndrome, lymphoproliferative disease, HIV, hepatitis B or C, drug-induced thrombocytopenia or previous treatment with hematopoietic growth factors or thrombopoietin receptor agonists were excluded. Control bone marrow samples were selected from patients 18 years of age or older with stage I to III lymphoma or monoclonal gammopathy of undetermined significance who had a normal platelet count within 4 weeks before bone marrow biopsy procurement. Prior treatment with anti-neoplastic agents or hematopoietic growth factors were exclusions. Bone marrow slides from ITP patients and controls were coded and arranged in random order. A standardized pathological assessment form was used to capture bone marrow cellularity (in deciles), diagnosis [‘ITP’ or ‘within normal limits (wnl)’], megakaryocyte number (increased, decreased or wnl), morphology (abnormal or wnl), distribution (abnormal or wnl) and megakaryocyte assessment on aspirate (abnormal or wnl). The form was piloted by a third independent hematopathologist (n=10 bone marrow examinations) and revised prior to duplicate review. All reviewers were pathologists with at least 5 years experience reading bone marrows. Chance-corrected agreement between reviewers was calculated using kappa (k) and chance-independent agreement was calculated using phi (φ) with 95% confidence intervals (CI). Results: Bone marrow slides were prepared from 30 ITP patients and 53 controls. Agreement on marrow cellularity (to within 20%), which was used to ensure calibration of assessors, was good (k= 0.75, 95% CI. 0.65–0.85). Of 81 evaluable bone marrows, pathologists agreed on the diagnosis for 69 cases (85.2%); overall agreement was fair (φ = 0.52; 95% CI: 0.10–0.78). Of the 30 ITP bone marrows, pathologists correctly agreed on the diagnosis in 2 (6.7%) cases, incorrectly agreed on 20 (66.7%) and disagreed on 8 (26.7%). Of the 51 control bone marrows, pathologists correctly agreed on the diagnosis in 46 (90.2%) cases, incorrectly agreed on 1 (2.0%) and disagreed on 4 (7.8%). Agreement on megakaryocyte number, morphology and distribution was fair. Conclusions: Inter-rater reliability of bone marrow examinations for the diagnosis of ITP was fair. Pathologists often incorrectly agreed that ITP bone marrows were considered to be within normal limits. These data suggest that ITP bone marrows often do not exhibit any distinguishing features. Disclosures: No relevant conflicts of interest to declare.

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)0.020
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.280
Teacher spread0.259 · 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 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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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