Evaluation of chimerism by quantitative PCR analysis of DNA polymorphism after allogeneic hematopoietic stem cell transplantation in a pediatric population with malignancies
Bibliographic record
Abstract
Bautista F, Moreno L, Fernández‐Navarro JM, Senent L, Andrés MM, Castel V, Verdeguer A. Evaluation of chimerism by quantitative PCR analysis of DNA polymorphism after allogeneic hematopoietic stem cell transplantation in a pediatric population with malignancies. Pediatr Transplantation 2011: 15:81–87. © 2010 John Wiley & Sons A/S. Abstract: Relapse remains the major pitfall to success for Allo‐HSCT in children with malignancies. Ninety‐one patients undergoing Allo‐HSCT were retrospectively reviewed. Chimerism status was evaluated at days +30, +60, and +100 in PB. VNTR‐PCR and STR‐PCR were used for this purpose. Thirty‐one patients recurred (34%) and none survived. Thirty‐two remain alive in CR (35%). Patients who achieved a CC at those days had a significant higher RFS and OS than patients who did not. Twelve patients showing PMC had an increased risk of recurrence (p = 0.02. OR 7.7). In the univariate analysis, the probability of death was higher in patients who were not in first CR before transplant (p = 0.008.OR 2.09) and in those receiving cells not from PB (p = 0.002.OR 2.03). In the multivariate analysis, the absence of CC at day +100 was associated with a higher probability of relapse (p = 0.004. OR 10.8) and death (p = 0.016. OR 9.3). Serial chimerism PCR‐based analyses of polymorphic DNA markers can predict relapse. Patients with PMC are at the highest risk of recurrence. Patients receiving an Allo‐HSCT in first CR from PB who achieve a CC at day +100 have a better outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".