MétaCan
Menu
← Back to cohort
Record W7006742286

Variability of Replicates of Intraocular Inflammatory Biomarkers in Ocular Fluid Samples Analyzed with Multiplex Assays

2023· article· en· W7006742286 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMultiplexIntraclass correlationReplicateBiomarkerAqueous humorBonferroni correction
DOInot available

Abstract

fetched live from OpenAlex

Daniel Lamoureux,1 David T Wong,2,3 Tina Felfeli2,4 1Northern Ontario School of Medicine University, Thunder Bay, ON, Canada; 2Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada; 3Department of Ophthalmology, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada; 4The Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, CanadaCorrespondence: Tina Felfeli, University Health Network, Toronto General Hospital, Eaton Building, 10th Floor, Toronto, ON M5G 2C4, Canada, Tel +1 647 678 1634, Fax +1 416 340 3459, Email tina.felfeli@mail.utoronto.caPurpose: Certain factors such as instrumental and sample processing errors may contribute to variability of ocular biofluid samples when they are run as replicates with multiplex assays. There is a paucity of literature on the variability of replicates in multiplex assays. This study aims to evaluate whether there is significant variability in replicate analyses of multiplex assays.Methods: A total of 152 human ocular biofluid samples (51 aqueous humor and 101 vitreous) were collected and assayed for 27 cytokine biomarker concentrations (pg/mL). Samples were evaluated as replicates (duplicate analysis) at four different time points. Statistical methods including paired samples t-test, 3-way ANOVA, intraclass correlation coefficient (ICC; < 0.5– 0.75=poor-moderate, 0.75-> 0.90 =good-excellent reliability), and coefficients of variation (CV) were employed to evaluate for statistical significance, with Bonferroni corrected P=0.002.Results: Among the 4104 biomarker replicate assays for aqueous humor and vitreous, two analytes (PDGF-BB and IL-7) had a statistically significant difference between the sampled concentrations of the replicates in vitreous samples (mean (diff)=2.05, P< 0.001, mean (diff)=1.56, P< 0.001, respectively). Majority of the ICC values fell within the good-excellent range (86% of samples) with a minority falling in the poor-moderate range (14% of samples). More variability was noted in the vitreous humour, with five analytes (IL-2, IL-10, IL-12(p70), IL-13, IL-17) demonstrating an average ICC of less than 0.5. The CV calculated for each set of replicates suggested that 93% of replicates had an acceptable level of quantitative assay variability (CV< 20%).Conclusion: This study demonstrates that the analysis of most biomarkers in ocular fluids may not require the use of replicates. However, certain analytes such as PDGF-BB and IL-7 may require the use of replicates to ensure reliable results. Caution should be taken when applying these findings to other laboratory settings as our study was conducted by an experienced technician using a standardized protocol. In less standardized settings, replicates may be required in order to ensure accuracy of results. These findings may guide researchers with the design of their studies on ophthalmic biomarker analysis.Keywords: cytokines, aqueous, vitreous, biomarkers, assay

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)0.022
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.129
GPT teacher head0.487
Teacher spread0.358 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)→Same topicGlaucoma and retinal disorders→French-language works237,207→