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Pitfalls in Interpreting Platelet Function Tests in Thrombocytopenic Patients Referred by a Hematologist for Diagnostic Testing: Results from a Single Center Prospective Study.

2005· article· en· W2566983112 on OpenAlexaff
Heather McKay, Jodi Seecharan, Karen A. Moffat, Nancy M. Heddle, Catherine P.M. Hayward

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsHamilton Regional Laboratory Medicine ProgramMcMaster University
Fundersnot available
KeywordsMedicinePlateletInternal medicineRistocetinMedical recordGastroenterologyAsymptomaticVon Willebrand diseaseVon Willebrand factor

Abstract

fetched live from OpenAlex

Abstract A recent survey of clinical laboratories indicated difficulties interpreting diagnostic platelet function testing when samples have reduced platelet counts. This is problematic considering the importance of such testing to establish diagnoses for some thrombocytopenic (TCP) disorders (e.g. Bernard Soulier Syndrome, BSS). Study goals: To evaluate findings and approaches suitable for interpreting platelet function results in TCP patients, a single center study was undertaken. Methods: Prospectively collected data on consecutively tested patients, referred for platelet function studies in Hamilton between May 1997–2005, was reviewed to identify individuals with TCP (defined as platelets <150x109/L). Medical records and laboratory databases were reviewed to obtain diagnostic information and laboratory results, including data for controls simultaneously tested at the same platelet count. Aggregation testing was done using platelet rich plasma (PRP), and most testing (80%) included: ADP, epinephrine, collagen and ristocetin, arachidonic acid and a thromboxane analague (U46619). % aggregation responses were compared to reference ranges (for samples with 250x109platelets/L) and diluted control results. Results: 17% (146/841) of individuals referred for testing had TCP. Information from medical records (available on 119/146 TCP patients) indicated diagnoses among patients included: TCP with platelet function defect (PFD; 61%), ITP (21%; 2 with associated PFD or acquired BSS), asymptomatic TCP (8%), TCP 2° to liver disease (4%), TCP 2° to known or suspected myelodysplasia (3%; 3 with PFD), lymphoma (n=1), von Willebrand disease (2%; types: 2B, n=2; 2M, n=1 with liver disease), Gaucher’s disease (n=1) and phospholipid antibody syndrome (n=1). TCP platelet disorders included: BSS (n=2; 1 acquired), asymptomatic MHY9 related disorder (n=1), autosomal dominant (AD) TCP with predisposition to AML (n=1), undefined AD TCP with PFD (n=15), and disorders with other/uncertain inheritance (n=23). Median (range) platelet counts (x109 /L) of patients were 99.5 (11–149) for CBC, and 159 (9–268) for PRP tested. 2% had micro TCP and 10% macro TCP. % aggregation data showed an influence of platelet count on responses in accumulated data from control tests, and for 4 controls tested with the full agonist panel at standardized reduced platelet counts. Most % aggregation responses for control samples with ≥150x109 platelets/L were within reference ranges, except with ADP and epinephrine. False positives were seen with low dose ristocetin. Many patients with TCP and PFD had reduced responses whereas those without PFD had responses similar to controls. The data from controls was important for diagnostic interpretations. Conclusions: Laboratories need to be cautious in interpreting platelet function tests when samples have reduced platelet counts. A strategy for interpreting findings, using accumulated information from controls tested at low platelet counts, is helpful. Currently, many individuals referred by hematologists for platelet function testing with TCP have PFD due to undefined problems, many of which appear to be inherited.

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)0.008
metaresearch head score (Gemma)0.020
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 score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.272
Teacher spread0.248 · 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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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