Evaluation of a Data-Derived Algorithm for Preemptive Use of Plerixafor for Stem Cell Harvest in Patients Eligible for Autologous Stem Cell Transplant (ASCT)
Bibliographic record
Abstract
Stem cell mobilization and collection is a prerequisite for ASCT. In 25-35% of patients, initial mobilization attempts fail to harvest sufficient peripheral blood stem cells (PBSC). The standard practice was the addition of plerixafor to the second mobilization attempt. A data-derived algorithm was developed to guide the use of plerixafor as a preemptive strategy, at first harvest, with the objective of preventing ASCT delays, risk of disease progression, and reducing patient stress. 25 patients were harvested for ASCT in the first year of implementation. The data-derived algorithm identified patients likely to fail mobilization based on a CD34+ cut-off for one or two ASCTs. Eight patients received preemptive plerixafor and were compared to 6 patients who received plerixafor as second line therapy in the previous year. The average number of doses of filgrastim for the second-line group was 16.5 vs 10.3 for the preemptive group, a decrease of 38%. The doses of plerixafor and days of apheresis were 2.33 vs 1.38 and 2.3 vs 1.6 for the second-line vs the preemptive group (a 41% and 30% decrease for preemptive). 83% of second-line patients proceeded to ASCT (1 progressed) vs 100% of preemptive patients at an average number of days from first harvest of 109 vs 47.7 days, respectively. Engraftment was identical. Figures 1 and 2 concur that the minimum PBSC count required to harvest enough for one or two ASCTs is 10x106/L and 2x106/L, respectively. The algorithm has optimized the use of plerixafor. The number of doses of plerixafor and filgrastim, the days of apheresis and the number of days to ASCT were all reduced. A successful first harvest attempt avoids transplant delays, reduces the risk of interim disease progression, avoids scheduling difficulties and reduces the stress to the patient.Figure 2View Large Image Figure ViewerDownload Hi-res image Download (PPT)View Large Image Figure ViewerDownload Hi-res image Download (PPT)
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".