Long‐term follow‐up of granulocyte colony‐stimulating factor receptor mutations in patients with severe congenital neutropenia: implications for leukaemogenesis and therapy
Bibliographic record
Abstract
Severe congenital neutropenia (SCN) is characterized by profound neutropenia, recurrent severe bacterial infections and maturation arrest in the myeloid lineage. Granulocyte colony-stimulating factor (G-CSF) treatment results in clinical improvement in over 90% of cases. Point mutations of the G-CSF receptor (G-CSFR) have been implicated in the progression of SCN to acute myeloid leukaemia (AML). Data are presented here on the 9-year follow-up of seven patients and the further screening of 18 other cases. One of the two original cases with a G-CSFR mutation has improved clinically; nevertheless, mutant DNA could still be detected at a very low level > 8 years after identification. The second child with a mutation progressed to myelodysplasia/AML 5 years after her mutation was detected. No mutations were found in the 18 new cases. One of three transformed cases had a G-CSFR mutation. This work is in agreement with the suggestion that G-CSFR mutations may provide a survival advantage to haemopoietic stem cells, but argues against the inevitability of leukaemic progression in their presence. Furthermore, the low frequency of G-CSFR mutations in SCN and the importance of regular screening and close clinical and laboratory follow-up if a mutation is found were demonstrated.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".