Very short telomere length by flow fluorescence in situ hybridization identifies patients with dyskeratosis congenita
Bibliographic record
Abstract
Dyskeratosis congenita (DC) is an inherited bone marrow failure syndrome in which the known susceptibility genes (DKC1, TERC, and TERT) belong to the telomere maintenance pathway; patients with DC have very short telomeres. We used multicolor flow fluorescence in situ hybridization analysis of median telomere length in total blood leukocytes, granulocytes, lymphocytes, and several lymphocyte subsets to confirm the diagnosis of DC, distinguish patients with DC from unaffected family members, identify clinically silent DC carriers, and discriminate between patients with DC and those with other bone marrow failure disorders. We defined "very short" telomeres as below the first percentile measured among 400 healthy control subjects over the entire age range. Diagnostic sensitivity and specificity of very short telomeres for DC were more than 90% for total lymphocytes, CD45RA+/CD20- naive T cells, and CD20+ B cells. Granulocyte and total leukocyte assays were not specific; CD45RA- memory T cells and CD57+ NK/NKT were not sensitive. We observed very short telomeres in a clinically normal family member who subsequently developed DC. We propose adding leukocyte subset flow fluorescence in situ hybridization telomere length measurement to the evaluation of patients and families suspected to have DC, because the correct diagnosis will substantially affect patient management.
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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 | 0.000 | 0.001 |
| 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".