Combined accurate platelet enumeration and reticulated platelet determination by flow cytometry
Bibliographic record
Abstract
BACKGROUND: Diagnosing the cause of thrombocytopenia often requires a bone marrow aspiration or biopsy, an invasive procedure. Reticulated platelets (RP) are immature RNA containing platelets, accurate RP enumeration has yet to be achieved, partially due to the lack of a robust reference method. GOAL: To refine previous work and gating strategies distinguishing RP from mature platelets while incorporating accurate platelet enumeration into the analysis. After reviewing previously published studies on Thiazole Orange (TO) staining of RP, we systematically evaluated CD41/CD61 in combination with a commercial source of TO (BDBiosciences). Previous RP methods have not taken advantage of platelet enumeration therefore our goal was to incorporate the ICSH platelet enumeration protocol into our method. METHODS: TO concentration, incubation, and fixation method were determined to be 10% of stock concentration, 30 min, and 1% formaldehyde respectively. Gating strategy to determine RP fraction used an unstained control tube to set the limit of TO staining. RESULTS: Normal range (n = 51) was 9.9 ± 3.1%. Analysis of 40 patients with immune-thrombocytopenia-purpura (ITP) showed a RP range from 4.3% to 81.2%. Platelet enumeration was consistent with our previous studies in this area. CONCLUSIONS: Combining CD41/CD61 platelet enumeration with TO RP percentage is possible. Accurate RP percentage requires an effective gating strategy, as background fluorescence cursor placement is important. This method for enumeration of RP percentage combined with accurate platelet enumeration, particularly in the low range, should prove useful in differentiating production from consumption issues in thrombocytopenia and monitoring response to therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".