Pott's disease in Moroccan children: clinical features and investigation of the interleukin-12/interferon-γ pathway
Bibliographic record
Abstract
SETTING: Tuberculosis spondylodiscitis (TS), or Pott's disease, an extra-pulmonary form of tuberculosis (TB), is rare and difficult to diagnose in children. Some cases of severe TB in children were recently explained by inborn errors of immunity affecting the interleukin-12/interferon-gamma (IL-12/IFN-γ) axis. OBJECTIVE: To analyse clinical data on Moroccan children with TS, and to perform immunological and genetic explorations of the IL-12/IFN-γ axis. DESIGN: We studied nine children with TS diagnosed between 2012 and 2014. We investigated the IL-12/IFN-γ circuit by both whole-blood assays and sequencing of the coding regions of 14 core genes of this pathway. RESULTS: A diagnosis of TS was based on a combination of clinical, biological, histological and radiological data. QuantiFERON(®)-TB Gold In-Tube results were positive in 75% of patients. Whole-blood assays showed normal IL-12 and IFN-γ production in all but one patient, who displayed impaired decreased response to IL-12. No candidate disease-causing mutations were detected in the exonic regions of the 14 genes. CONCLUSIONS: TS diagnosis in children remains challenging, and is based largely on imaging. Further investigations of TS in children are required to determine the role of genetic defects in pathways that may or may not be related to the IL-12/IFN-γ axis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".