MétaCan
Menu
Back to cohort
Record W2974638572

Impact of hemolysis during sample collection: How different is drug concentration in hemolyzed plasma from that of normal plasma?

2012· article· en· W2974638572 on OpenAlexaff
Aimin Tan, Sébastien Gagné, Isabelle A. Lévesque, Sylvain Lachance, Nadine Boudreau, Ann Lévesque

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsBioPhage Pharma (Canada)
Fundersnot available
KeywordsHemolysisChemistryDrugPlasmaPlasma concentrationChromatographyPharmacologyRed blood cellBiochemistryInternal medicineMedicine
DOInot available

Abstract

fetched live from OpenAlex

Hemolysis is a common phenomenon in clinical studies. Despite the growing interest in hemolysis matrix effect, how hemolysis impacts the representability of hemolyzed plasma samples was rarely evaluated. The purpose of this research is to perform such an evaluation by theoretical consideration and experiment. A formula for estimating the impact is proposed, which includes the degree of hemolysis and the drug's red blood cell (RBC): plasma concentration ratio. The impact of hemolysis on the representability of hemolyzed plasma samples is compound-dependant. Given the same degree of hemolysis, the stronger a drug binds to RBCs, the more significant the impact of hemolysis. For a drug with high affinity to RBCs, the results of hemolyzed plasma samples may not be useful even though they are accurate. There is an overall agreement between theoretical predication and experimental results. Among the ten different drug compounds tested, only methazolamide, which binds strongly to RBCs, showed significant change in plasma concentration due to hemolysis.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.333
Teacher spread0.291 · 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.

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
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicClinical Laboratory Practices and Quality ControlFrench-language works237,207