Rationale for using insensitive quality control rules for today’s hematology analyzers
Bibliographic record
Abstract
Diverse approaches have been used to assure the analytical quality of automated hematology; as such, there is great variation in their error detection capabilities. We summarize the intralaboratory performance of a cohort of Sysmex XE-2100's running e-Check hematology quality control (QC). The imprecisions of a median performing (50th percentile imprecision) and more imprecise [15th percentile (15P) imprecision] Sysmex XE-2100 are compared with measures of total allowable error (regulatory and physiologically based) to obtain multiples of the usual imprecision that must be detected to prevent the hematology analyzer from producing medically unacceptable results. The resultant large multiples of the usual imprecision (s) demonstrate the need for insensitive QC rules employing very broad control ranges, control rules that have been implicitly supported by hematology analyzer manufacturers for the last several decades. For today's highly precise hematology analyzers, the following control rules are strongly advised: 1(3.5s) , 1(4s) and 1(4.5s) rules (violated if a single control observation exceeds either its ±3.5, ±4.0 and ±4.5s limits, respectively). In order for the hematology laboratory to totally embrace expanded QC limits, manufacturers must make available their instruments' usual and poorer (e.g. the 15P performance) imprecision's. Users of hematology analyzers that require more sensitive but less specific rules to prevent the reporting of clinically erroneous data are advised to acquire more precise (and thus more dependable) instrumentation.
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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 | 0.239 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.001 | 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".