Associations among white blood cells, CD34+ cells and GM-CFU in predicting the optimal timing of peripheral blood stem cell collections by apheresis.
Bibliographic record
Abstract
AIMS: To analyze preapheresis blood CD34+ cells and corresponding apheresis products in order to investigate whether peripheral blood CD34+ cell counts correlate with peripheral blood progenitor cell (PBPC) yields and to determine the optimal timing for starting PBPC collections in our clinical setting. MATERIAL AND METHODS: Thirty-eight patients with hematological malignancies and undergoing varied mobilization regimens were enrolled. White blood cell counts (WBC), CD34+ cells and granulocyte-macrophage colony-forming units (GM-CFU) enumeration were performed on blood samples taken immediately prior to each apheresis procedure and in the corresponding PBPC collection. RESULTS: A total of 61 apheresis procedures were performed, with a median of two collections per patient (range 1-3). The number of CD34+ cell/ml in the preapheresis blood correlated closely with CD34+ cells/kg and, to a lesser degree, with GM-CFU/kg in the apheresis products (r = 0.81 and r = 0.67, respectively, P < 0.0001). WBC showed significant but poor correlation with CD34+ cells/kg and GM-CFU/kg (r = 0.43 and r = 0.45, respectively, P = 0.004). A significant correlation was also found between CD34+ cells/kg and GM-CFU/kg in PBPC collections (r = 0.62, P < 0.0001). Linear regression analysis indicated that the minimum threshold of 2 x 10(6) CD34+ cells/kg might be attained with a single apheresis if the CD34+ cells/ml in the peripheral blood measured prior to apheresis on the day of collection is > or =26 x 10(3) CD34+ cells/ml. CONCLUSION: Collectively, these data demonstrate that circulating CD34+ cells is more useful than GM-CFU or WBC for predicting the optimal timing of PBPC harvests.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".