Identification of the CD34 enumeration on the day before stem cell harvest that best predicts poor mobilization
Bibliographic record
Abstract
BACKGROUND: We questioned whether CD34 enumeration in peripheral blood on the day before planned collection would identify a patient population that could benefit from an augmented collection strategy during that mobilization attempt. STUDY DESIGN AND METHODS: A retrospective review of all adult patients who underwent a first mobilization attempt between January and December 2008 for autologous use was undertaken. Peripheral blood CD34 quantitation on the day before planned collection (Day -1) and day of planned collection (Day 0) was correlated with likelihood of a successful collection. RESULTS: Of 41 patients (15 with multiple myeloma, 20 with lymphoma, and six with other malignancies) who underwent mobilization 24 patients (58%) were harvested in 1 day (good mobilizers) with the remaining 17 patients (42%) either requiring more than 1 day to collect or were not collected (poor mobilizers [PMs]). A peripheral blood CD34+ count below 10 × 10(6) /L on Day -1 was optimal in identifying PMs (adjusted odds ratio of 7 [1.4-35]). Increasing the CD34 cutoff from 10 × 10(6) to 15 × 10(6) /L decreased the prediction of poor mobilization (positive likelihood ratio dropped from 3.3 to 2.2). CONCLUSION: Peripheral blood CD34 content of less than or equal to 10 × 10(6) CD34+ cells/L on the day before collection is predictive of poor mobilization whereas higher peripheral blood CD34 counts on Day -1 have a high likelihood of successful 1-day collection.
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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 | 0.005 |
| 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 | 0.001 |
| 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 | 0.000 |
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".