Plasma Factors for the Differentiation of Hodgkin’s Lymphoma and Diffused Large B Cell Lymphoma and for Monitoring Remission
Bibliographic record
Abstract
BACKGROUND: Hodgkin lymphoma (HL) is one of the most frequent cancers occurring at a young age. Although diagnosis of HL is not difficult, a minimally invasive method to diagnose HL, and a radiation-free method to confirm the remission status are highly desired. METHODS: In this study, we employed cutting-edge Luminex technology to evaluate 67 soluble plasma proteins for their suitability for diagnosis and for confirming remission of classical HL (cHL). RESULTS: Soluble cluster of differentiation (CD)30 and CC motif chemokine ligand (CCL)22 were identified to be capable of differentiating cHL patients from healthy donors and from patients with diffuse large B cell lymphoma (DLBCL), a disease that shares many characteristics with cHL. Soluble tumor necrosis factor receptor (TNFR)2 was found to be lower in the remission than in the initial diagnosis cohort of cHL patients, and also to be lower in plasmas at remission than in plasmas at initial diagnosis from the same patients. In DLBCL plasmas, concentrations of interleukin (IL)-2, soluble IL-2 receptor and IL-31 changed in patients upon entering remission. CONCLUSIONS: Measurement of these factors may: 1) provide a minimally-invasive method to diagnose and differentiate HL and DLBCL, and 2) make it possible to monitor the remission status of these patients without use of radiation-based imaging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".