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Record W2103095180 · doi:10.1002/pbc.22988

Platelet disorders in children: A diagnostic approach

2011· article· en· W2103095180 on OpenAlexaff
Sara J. Israels, Walter H.A. Kahr, Victor S. Blanchette, Naomi L.C. Luban, Georges E. Rivard, Margaret L. Rand

Bibliographic record

VenuePediatric Blood & Cancer · 2011
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsHospital for Sick ChildrenCentre Hospitalier Universitaire Sainte-JustineUniversity of ManitobaChildren's Hospital Research Institute of ManitobaManitoba Health
Fundersnot available
KeywordsMedicinePlateletPlatelet disorderBlood cancerMucocutaneous zoneFlow cytometryMedical diagnosisBlood Platelet DisordersPathologyImmunologyCancerInternal medicinePlatelet aggregation

Abstract

fetched live from OpenAlex

The investigation of children with suspected inherited platelet disorders is challenging. The causes of mucocutaneous bleeding are many, and specialized testing for platelet disorders can be difficult to access or interpret. An algorithm developed for the investigation of suspected platelet disorders provides a sequential approach to evaluating both platelet function abnormalities and thrombocytopenia. Investigation begins with a clinical evaluation and laboratory testing that is generally available, including platelet counting, peripheral blood cell morphology, and aggregometry. Based on results of initial investigations, the algorithm recommends specialized testing for specific diagnoses, including flow cytometry, immunofluorescence microscopy, electron microscopy, and mutational analysis.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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
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

Citations80
Published2011
Admission routes1
Has abstractyes

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