Bibliographic record
Abstract
A newborn girl with ileal atresia presented with bilious emesis and bowel perforation. Automated blood count using an XN-9000 analyser (Sysmex, Kobe, Japan) run within 30 min of collection showed a haemoglobin of 106 g/L, white blood cell count of 0.7 × 109/L and platelets of 1061 × 109/L with very low mean platelet volume (5.8 fL). The platelet histogram showed an abnormal shift towards small-sized particles (left panel, top), and the fluorescent platelet scattergram showed an unidentified population (left panel, bottom, red arrow). Blood smear examination revealed extensive bacteraemia with both intracellular and extracellular rod-shaped organisms (right three panels, 100× objective, Wright–Giemsa stain). The blood cultures were positive for multidrug-resistant Escherichia coli. By manual estimation, the platelets were in fact markedly decreased (right panel, green arrow). She passed away following a partial bowel resection. Sysmex haematology analysers measure platelet count via impedance, which is based on changes in electrical current as particles pass through an aperture. This allows the analyser to identify and separate particles based on size. Falsely elevated platelet count (pseudothrombocytosis) can be caused by the presence of other particles with similar size to platelets. When interference is detected, the measurement is reflexed to a fluorescence-based method, which uses oxazine to stain the rough endoplasmic reticulum and mitochondria in platelets. Although the presence of microorganisms in the blood smear itself is not uncommon, florid bacteraemia to the degree of causing a spuriously increased platelet count is very rarely reported (mainly in older instruments).1, 2 Furthermore, in our case, the interference was observed in both impedance and fluorescence methods, likely due to an inability of the analyser to discriminate platelets from microorganisms with similar size, diffraction and/or cross-reaction with the fluorescent dye. This case demonstrates that modern haematology analysers are not exempt from such interference, and highlights the importance of a careful review of the scattergrams and blood smear in resolving the discrepancy.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".