Within-Individual Mean Corpuscular Volume Variation
Bibliographic record
Abstract
We read with interest “Biological Variations of Hematologic Parameters Determined by UniCel DxH 800 Hematology Analyzer.”.1 Most of the estimates of biologic variation of the common analytes reported by Zhang et al1 were close to previously reported literature values. We are puzzled, however, about the broad intraindividual variation for mean corpuscular volume (MCV; 1.12%). Although compilations of biologic variation published before 2000 indicated intraindividual MCV coefficients of variation (CVs) could exceed 1%, 2 recent studies determined the CV was much lower, either 0.18%2 or 0.34%.3 As a result, the Zhang et al reference change values for MCV are much higher than our intuitive reference change values (perhaps, 2 fL) when we examine sequential patient MCVs measured by analyzers that are much older than the analyzer used by Zhang et al.The larger intraindividual MCV CVs reported before 2000 were likely associated with the use of more-imprecise instrumentation. Today, our analytic variations in MCV measurements are miniscule.4 Reasons for a tripling or quadrupling of the estimated intraindividual CV might include the incorporation of some outlying data, analyzer noise, or even some unusual individual-specific variation. We are wondering if Zhang et al would reexamine their MCV data and correlate any available quality control results with those data. Could this variation have been due to some sporadic instrument or individual source?
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".