Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
INTRODUCTION: The Beckman Coulter DxH 300™ is a hematology analyzer that performs a CBC and 3-part WBC differential and incorporates new electronic, algorithm and mechanical design. METHODS: This instrument was compared with the predicate analyzer (Coulter(®) A(c) ·Tdiff2) and the larger format Coulter LH780 analyzer. Of interest were flagging rates, clinical sensitivity, and accuracy of the WBC in the presence of interfering particles. The total sample set (n = 404) consisted of morphologically normal and hematologically abnormal patients. RESULTS: Correlation of the DxH 300 with A(c) ·Tdiff2™ showed good agreement with all directly measured parameters. When compared with the LH780, WBC and platelets (PLT) counts showed good agreement with small biases. Importantly in the low range (PLT <50 × 10(9) /L), there was a small positive bias of only 2 × 10(9) /L PLT. Interfering particles did not affect the DxH 300 WBC count (P > 0.05) with strong correlations to the LH780 (r(2) values >0.95). Importantly, overall and specific flagging rates as well as false-negative and false-positive rates were significantly reduced on the DxH 300 compared with the A(c) ·Tdiff2 (27% and 41% reduction, respectively, P < 0.001). CONCLUSIONS: The DxH 300 offers significant improvement over the predicate Coulter analyzer in flagging rates and improved correlation with larger format analyzers for WBC and PLT counts. Reduced false negatives and false positives significantly improved sensitivity and specificity compared to the predicate analyzer. The 28% improvement in flagging efficiency together with numerous software and data handling enhancements should translate into reduced need to perform follow-up analysis on a significant number of samples.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.377 | 0.221 |
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".