<i>In‐situ</i> follicular neoplasia: a clinicopathological spectrum
Bibliographic record
Abstract
AIMS: In-situ follicular neoplasia (ISFN) occurs in approximately 2-3% of reactive lymph nodes, and is currently set apart from 'partial involvement by follicular lymphoma' (PFL). ISFN can progress to overt lymphoma, but precise parameters with which to assess this risk and its association with related diseases remain incompletely understood. The aim of this study was to explore these parameters. METHODS AND RESULTS: We reviewed 11 cases of ISFN and one of PFL between 2003 and 2018. Ten patients had ISFN in the lymph nodes, and one had ISFN in the spleen. Haematoxylin and eosin and immunohistochemical stains were reviewed. Involvement of follicles by ISFN was scored with a three-tier scheme. Of five patients with low ISFN scores, one had chronic myelomonocytic leukaemia, one had mycosis fungoides, and three were free of haematopoietic disease. Among them, four are alive and one was lost to follow-up. Of the six ISFN patients with high scores, two had concurrent marginal zone lymphomas, one had concurrent diffuse large B-cell lymphoma (DLBCL), one had Castleman-like disease, one had progressive transformation of germinal centres with IgG4-related disease, and one had no haematopoietic disease; all are alive except for one who died of concurrent DLBCL. The patient with PFL developed DLBCL 7 years after diagnosis. CONCLUSIONS: On the basis of this limited series, we conclude that only cases with high scores are associated with an overt lymphoma or an abnormal lymphoid process, and that scoring may be a useful parameter with which to assess the risk of associated lymphoma, and deserves further study. We also performed a comprehensive review of the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".