Small volume biopsy diagnostic yield at initial diagnosis versus recurrence/transformation of follicular lymphoma: A retrospective Cyto‐Heme Interinstitutional Collaborative study
Bibliographic record
Abstract
BACKGROUND: Few studies have evaluated diagnostic yield of small volume biopsies (SVB) for the diagnosis and management of follicular lymphoma (FL). METHODS: The authors performed a multi-institutional retrospective analysis of SVBs including fine-needle aspiration (FNA) and needle core biopsy (NCB) for initial FL diagnosis and suspected recurrence or transformation of FL. A total of 676 workups beginning with SVB were assessed for the mean number of biopsies per workup, the proportion of workups requiring multiple biopsies, and the proportion with a complete diagnosis including grade, on initial biopsy. RESULTS: Compared to workups performed for question transformation/recurrence, those done for initial FL diagnosis were significantly more likely to require multiple biopsies (p < .01), had a higher mean number of biopsies per workup (1.7 vs. 1.1, absolute standardized difference = 1.1), and a lower complete diagnosis rate at initial biopsy (39% vs. 56%). At initial FL diagnosis, NCB +/- FNA was associated with fewer biopsies per workup compared to FNA +/- CB (1.2 vs. 1.9), fewer workups requiring multiple biopsies (23% vs. 83%), and a higher complete diagnosis rate (71% vs. 18%). In contrast, during assessment for transformation/recurrence, NCB and FNA showed a similar mean number of biopsies per workup (1.2 vs. 1.2) and few workups required multiple biopsies (6% vs. 19%). CONCLUSIONS: SVB at initial FL diagnosis often required additional biopsies to establish a complete diagnosis. In contrast, when assessing for transformed/recurrent FL, additional biopsies were generally not obtained regardless of SVB type, suggesting that in these clinical settings SVB may be sufficient for clinical decision-making.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".