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Record W2293936845

Comparison of the Lactate Pro and the YSI 1500 Sport Blood Lactate Analyzers

2004· article· en· W2293936845 on OpenAlexaff
S R McLean, Stephen R. Norris, D. J. Smith

Bibliographic record

VenueInternational Journal of Applied Sports Sciences · 2004
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBlood lactateLimits of agreementCycle ergometerSpectrum analyzerLinear regressionMathematicsReliability (semiconductor)Nuclear medicineChemistryMedicineStatisticsInternal medicinePhysicsBlood pressurePower (physics)Heart rate
DOInot available

Abstract

fetched live from OpenAlex

To determine the agreement, reliability, and linearity of the Lactate Pro with the YSI 1500 Sport lactate analyzer, seven male and five female volunteers performed a discontinuous incremental exercise test at discrete percentages of their maximum aerobic power on a cycle ergometer. Five blood samples were collected for each subject; one after each of five increasing workloads. Agreement was evaluated by comparing, in parallel, measurements from the Lactate Pro and the YSI 1500 using Bland-Altman plots. Reliability was determined by performing 10 repeated assays on blood collected at 160 W and 370 W in a randomly selected subject. Strength of association was determined by performing linear regression analysis of the measures obtained from both analyzers. Bland-Altman analysis revealed that blood lactate concentrations from the Lactate Pro were 0.5 ± 1.0 mM (mean ± SD) higher than the YSI 1500 with the limits of agreement being 0.2 to 0.7 mM. Coefficients of variation from blood collected at 80 W and 370 W were 7.1% and 8.9%, respectively. The linear regression equation was Lactate Pro = 1.067(YSI 1500) + 0.036 (R2 = 0.967). In conclusion, the Lactate Pro is reliable and has sufficient agreement and linearity with the YSI 1500 for use in submaximal research and athletic testing.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.326
Teacher spread0.306 · 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

Citations20
Published2004
Admission routes1
Has abstractyes

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