Clinical and molecular features of immunodeficiency in patients with telomere biology disorders
Bibliographic record
Abstract
ABSTRACT: Immunodeficiency in telomere biology disorders (TBDs) has been described in pediatric patients with severe phenotypes, but is less characterized within the broader TBD spectrum. We collected complete blood counts, lymphocyte subsets, and infection history from 88 consecutive patients with TBD with a median age of 38 years (range, 6-76). Most patients were >18 years old (80/88; 90%) and harbored either a TERT (45%) or TERC germ line mutation (32%). Thirty-two patients (36%) experienced significant infections (opportunistic, recurrent, and/or requiring hospitalization); 47% had lymphopenia, and 3% severe neutropenia. Absolute lymphocyte counts (ALCs) of <0.96 and <1.1 × 103/μL, but not severe neutropenia, were associated with increased infection risk and lower overall survival, respectively. Decreased CD3+ T cells, both CD4+ and CD8+, were associated with bone marrow failure, increased infection risk, and reduced survival. Low CD3+ and CD4+ T cells were associated with solid cancers. Telomere length was shortened across the cohort without correlation with ALC or lymphocyte subsets. In a predominantly adult cohort of TBDs, immunodeficiency was marked by T-cell lymphopenia, possibly a consequence of accelerated aging in the hematopoietic compartment. An ALC cutoff of <1.1 × 103/μL may be a useful biomarker to identify patients with an increased risk of infection, a major cause of death in patients with TBD.
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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 | 0.001 |
| 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) | 0.002 | 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".