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Record W1968383346 · doi:10.1309/ajcpymfxznsnrz55

Results of an External Proficiency Testing Exercise on Platelet Dense-Granule Deficiency Testing by Whole Mount Electron Microscopy

2009· article· en· W1968383346 on OpenAlexaff
Catherine P.M. Hayward, Karen A. Moffat, Ernie Spitzer, Marnie Timleck, Elizabeth Plumhoff, Sara J. Israels, James G. White

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2009
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsMcMaster UniversityUniversity of ManitobaHamilton Regional Laboratory Medicine Program
Fundersnot available
KeywordsGranule (geology)PlateletExternal quality assessmentMedicineDense granulePathologyInternal medicinePlatelet aggregationBiology

Abstract

fetched live from OpenAlex

Performance on specialized diagnostic tests for platelet disorders, including dense-granule deficiency, is rarely evaluated by external quality assessment (EQA). Members of the North American Specialized Coagulation Laboratory Association that evaluate platelet dense-granule deficiency commonly use whole-mount electron microscopy (EM) methods. This observation led us to develop a pilot EQA survey with standardized EM images and clinical samples on grids from a healthy control subject and a subject with dense-granule deficiency. The survey participants were 8 centers, including 2 with no experience in platelet whole mount EM. All participants, including inexperienced sites, correctly interpreted findings for the normal and dense-granule-deficient platelets. Among experienced sites, agreement was excellent (>82%) on platelet structures to count or not count as dense granules. Participants indicated that future EQA challenges should include clinical samples on grids and standardized images. This is the first report that platelet EM can be assessed by EQA.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.383
Teacher spread0.349 · 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

Citations52
Published2009
Admission routes1
Has abstractyes

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