The Severe Chronic Neutropenia International Registry - 10 Years of Follow-Up.
Bibliographic record
Abstract
Abstract 2004 marks the 10th anniversary of the Severe Chronic Neutropenia International Registry (SCNIR), a registry organized to improve understanding and treatment of the hematological disorders causing severe chronic neutropenia. The SCNIR enrolls patients with blood neutrophil counts intermittently or continuously less than 0.5x109/L whose neutropenia is not attributed to cancer, cancer chemotherapy, or systemic autoimmune diseases. Longitudinal data has now been collected on 1163 patients with a range of follow-up of 0.01 to 15.66 years. By diagnostic category the patients include severe congenital neutropenia (422), cyclic neutropenia (205), idiopathic neutropenia (349), autoimmune neutropenia (68), glycogen storage disease (42), Barth syndrome (10), myelokathexis (8), Shwachman-Diamond syndrome (37), immune deficiency syndromes (7), and others (15). Overall, 1053 (90.5%) of these patients have been treated longitudinally with G-CSF (dose range 0.02 to 300 mcg/kg/day, median 3.33 mcg/kg/day) or received G-CSF transiently. Among other treatments, 76 patients were treated with allogeneic bone marrow or stem cell transplants (SCT). The reasons for SCT were myelodysplasia (MDS) or acute myeloid leukemia (AML) (43), chromosomal aberrations or G-CSF receptor mutation (9), partial or non-response to G-CSF (21), or other reasons (3). G-CSF has dramatically changed the natural history of these disorders reducing the occurrence of infection, hospitalization, and antibiotics, and improving patients’ quality of life. A small percentage of patients, 14% (163/1163), show evidence of osteoporosis/osteopenia; the predisposing factors for this complication remain unclear. MDS and AML has occurred in 63 patients: severe congenital neutropenia (13.7%, 58/422), Shwachman-Diamond syndrome (8.1%, 3/37) and 2 others with the clinical diagnoses of cyclic neutropenia (1) and idiopathic neutropenia (1). The SCNIR has also provided a rich resource for studies on the genetic and molecular basis for these disorders. These include the finding of mutations in the gene for neutrophil elastase (ELA2) in causing cyclic and congenital neutropenia, the role of mutations in the gene for the G-CSF receptor in the evolution of severe congenital neutropenia to AML and the importance of apoptosis as the cellular mechanism for several diseases causing severe chronic neutropenia. The SCNIR illustrates the value of a patient registry to improve our understanding of rare hematological diseases.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".