What causes lymphopenia in primary lymphatic anomalies? Implications for understanding lymphocyte homeostasis
Bibliographic record
Abstract
Recent studies in patients with primary lymphatic anomalies (PLA) have identified hitherto unrecognized levels of lymphopenia [1 ]. PLA represent a cluster of developmental disorders caused by inborn errors of lymphovascular development presumed genetic in origin. Present at birth or developing in early childhood/adult-life, they range widely in anatomical extent and severity. Observations of lymphopenia in PLA have wider implications for human lymphocyte biology as they demonstrate the importance of interactions between lymphatic architecture and lymphocyte phenotype and function. This Perspective paper invokes potential mechanistic explanations for lymphoedema-associated lymphopenia in PLA and considers wider implications for understanding human lymphocyte homeostasis. In a recent study, investigating blood lymphocyte counts in a large cohort of patients with PLA, we noted four striking features [1]: (i) lymphopenia is common and often severe; (ii) lymphopenia is specific—not part of a generalized leukopenia; (iii) different cell types are affected differentially—naïve CD4+ T cells being most affected; and (iv) different patterns of lymphopenia co-segregate with different clinical phenotypes, the more severe patterns being associated with involvement of central lymphatics (‘systemic’ disease) as opposed to disease confined to localized tissue (‘simplex’).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".