Bibliographic record
Abstract
The performance characteristics of the XE-2100 (Sysmex, Kobe, Japan) automated immature granulocyte (IG) count were studied. The automated IG count was compared with the manual morphology count and with a proposed reference flow cytometric count. The comparison data were analyzed by both least-squares and Passing-Bablok regression analysis. Long-term imprecision using preserved blood quality control specimens at different levels showed a range from 2.59% to 3.57% coefficient of variation (CV) for within-run imprecision and 3.57% to 6.85% CV for total imprecision. The within-run reproducibility performed using fresh blood on 3 different specimens showed a range from 5.55% to 8.24% CV. The counts were stable at both room temperature and after refrigeration for 24 hours.Passing-Bablok regression analysis showed excellent agreement between the proposed reference flow cytometric IG count and the XE-2100 IG count, while there was less agreement with the manual morphology count. Our results indicate that the automated IG count can replace the manual morphology count for IG counting in the clinical laboratory. The results also confirm that the flow cytometric IG count is superior to and can replace the manual morphology count as a reference method for IG counting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".