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Blood Microsampling for Complete Blood Count: Take Heed of Preanalytical Errors

2017· article· en· W2755170426 on OpenAlexvenueno aff
Barbara Kościelniak, Andrzej Zając, Przemysław Tomasik

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsMedicine

Abstract

fetched live from OpenAlex

Background: The credibility of the result of a complete blood count is closely connected with the preanalytical phase. Objectives: This study evaluated accordance of filling of microtubes with manufacturer’s recommendation and assessed the effect of storage of overfilled and underfilled samples on the results of complete blood count. Design and Methods: Volume of blood samples collected into microtubes in the wards of the University Children's Hospital in Cracow during one month was analyzed. In the stability studies, overfilled and underfilled samples stored at ambient temperature were analyzed at 1, 2, 3 and 12 hours after phlebotomy. The analysis was made using the SYSMEX XT-1800i analyzer. Results: More than half of the analyzed samples were incorrectly filled. 63% of the samples were filled above the manufacturer's recommended volume and 15% of test-tubes were filled below the recommendation. We observed differences between collected blood volume in accordance to the age of patients (p=0.001). The storage of overfilled and underfilled microtubes for complete blood count for 1,2,3 and 12 h at room temperature had no effect on the results of this test. Conclusion: Medical staff does not follow the instructions of the manufacturers. It might lead to a decrease of the quality and credibility of the results.

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)0.001
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 score0.508
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.079
GPT teacher head0.431
Teacher spread0.352 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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