Bibliographic record
Abstract
Background: For many years, relative values based on 100 quantified cells have been used to assess blood counts in the field of hematology. However, modern blood counting machines have recently made it possible to determine absolute counts. Thus, the current study assessed whether the determination of relative values, based on 100 cells counted, or the determination of absolute values is more accurate in hematology.\nMethods: To calculate the errors of absolute counts and of quotients, we used two independent methods to determine the errors. For the error calculation, we first performed a Gaussian error calculation. Second we identified the errors using daily control checks and examined the high limit of the actual errors (precision) on the Sysmex XE5000 hematological analyzer.\nResults: Our findings indicated that the accuracy of the relative values was always much higher compared to the absolute values.\nConclusion: This finding can be explained by combined errors which affect absolute cell counts and which are directed for all cell counts of one run into the same direction. These types of errors are reduced by quotient formation as shown here for the basophils. The accuracy of the absolute values obtained from the hematology machines of the latest generation was acceptable due to the very high number of cells quantified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".