Ultrasound-guided lymph node biopsy sampling to study the immunopathogenesis of rheumatoid arthritis: a well-tolerated valuable research tool
Bibliographic record
Abstract
BACKGROUND: Analyses of lymphoid organs are required to further elucidate the pathogenesis of inflammatory diseases like rheumatoid arthritis (RA). Yet, invasive tissue collection methods are scarcely applied, because they are often considered burdensome, although patients do not always consider invasive methods as a high burden. We aimed to investigate the perspectives of study participants undergoing ultrasound-guided inguinal lymph node (LN) needle biopsy sampling and determine the molecular and cellular quantity and quality of LN biopsies. METHODS: Together with patient research partners, questionnaires were developed to evaluate the motives, expectations, and experiences of participants undergoing a LN biopsy. Healthy controls and RA(-risk) patients were asked to complete these questionnaires before and after the procedure. RNA and lymphocyte yields from obtained LN biopsies were also calculated. RESULTS: We included 50 individuals, of which 43 (86%) reported their pre- and post-procedure experiences. The median reported pain on a 5-point Likert scale (1 not to 5 very painful) was 1. Interestingly, almost all (n = 32; 74%) study participants would undergo a second procedure and more than half (n = 23; 54%) would encourage others to take part in the LN biopsy study. Motives for current and future participation were mostly altruistic. Inguinal hematoma occurred frequently, but no other significant or unexpected complications ensued. The LN biopsies yielded sufficient and high-quality RNA and lymphocyte numbers. CONCLUSIONS: Ultrasound-guided inguinal LN biopsy sampling is well-tolerated, safe, and provides sufficient material for further molecular and cellular analyses. Our participants' positive experiences endorse the application of this research tool to further elucidate the pathogenesis of RA and other inflammatory diseases.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".