Transcervical thymic biopsy in the immunodeficient child
Bibliographic record
Abstract
Objective: The objectives of this study are to present a case series of immunodeficient children who underwent a transcervical thymic biopsy and to describe the transcervical approach to the thymus gland. Design: Case series. Setting: Pediatric otolaryngology practice in an academic setting. Patients: Consecutive sample of immunodeficient children (≤18 years old) who underwent thymic biopsies from 1996 to 2019 for the purpose of confirming or excluding profound T cell immunodeficiency. Intervention: Diagnostic transcervical thymic biopsy. Results: A total of 14 patients with atypical combined immunodeficiency underwent the procedure during the study period, with minimal post-operative complication. The thymus was found to be abnormal histologically in 9 children and normal in another 5 patients. In all cases, thymus morphology helped define the extent of the immunodeficiency, resulting in either supporting a decision to perform a bone marrow transplant (8 patients) or avoid this high risk procedure (3 patients). Conclusion: Thymus biopsy is helpful in the characterization of childhood immunodeficiency and provides critical information that affects the medical management. The transcervical approach to the thymus is feasible in children and can be accomplished with minimal morbidity. Statement of novelty: Biopsies of the thymus have assisted in the characterization of new entities of primary immunodeficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".