Bibliographic record
Abstract
The accurate distinction of reactive and neoplastic lymphoid proliferations can present challenges. Given the different prognoses and treatment strategies, a correct diagnosis is crucial. Molecular clonality assays assess rearranged lymphocyte antigen receptor gene diversity and can help differentiate reactive from neoplastic lymphoid proliferations. Molecular clonality assays are commonly used to assess atypical, mixed, or mature lymphoid proliferations; small tissue fragments that lack architecture; and fluid samples. In addition, clonality testing can be utilized to track neoplastic clones over time or across anatomic sites. Molecular clonality assays are not stand-alone tests but useful adjuncts that follow clinical, morphologic, and immunophenotypic assessment. Even though clonality testing provides valuable information in a variety of situations, the complexities and pitfalls of this method, as well as its dependency on the experience of the interpreter, are often understated. In addition, a lack of standardized terminology, laboratory practices, and interpretational guidelines hinders the reproducibility of clonality testing across laboratories in veterinary medicine. The objectives of this review are twofold. First, the review is intended to familiarize the diagnostic pathologist or interested clinician with the concepts, potential pitfalls, and limitations of clonality testing. Second, the review strives to provide a basis for future harmonization of clonality testing in veterinary medicine by providing diagnostic guidelines.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".