Quality control technique to reduce the variability of longitudinal measurement of hemoglobin mass
Bibliographic record
Abstract
The sensitivity of the athlete blood passport to detect blood doping may be improved by the inclusion of total hemoglobin mass (Hb(mass)), but the comparability of Hb(mass) from different laboratories is unknown. To optimize detection sensitivity, the analytical variability associated with Hb(mass) measurement must be minimized. The aim of this study was to investigate the efficacy of using quality controls to minimize the variation in Hb(mass) between laboratories. Three simulated laboratories were set up in one location. Nine participants completed three carbon monoxide (CO) re-breathing tests in each laboratory. One participant completed two CO re-breathing tests in each laboratory. Simultaneously, quality controls containing Low (1-3%) and High (8-11%) concentrations of percent carboxyhemoglobin (%HbCO) were measured to compare hemoximeters in each laboratory. Linear mixed modeling was used to estimate the within-subject variation in Hb(mass), expressed as the coefficient of variation, and to estimate the effect of different laboratories. The analytic variation of Hb(mass) was 2.4% when tests were conducted in different laboratories, which reduced to 1.6% when the model accounted for between-laboratory differences. Adjustment of Hb(mass) values using quality controls achieved a comparable analytic variation of 1.7%. The majority of between-laboratory variation in Hb(mass) originated from the difference between hemoximeters, which could be eliminated using appropriate quality controls.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".