Beyond The Node : Atypical Presentations of Non-Hodgkinu2019S Lymphoma
Bibliographic record
Abstract
Beyond the Node: Atypical Presentations of Non-Hodgkinu2019s LymphomaAuthors: Jessica L. Dobson (BSc) & Dr. Lisa Smyth (MD FRCPC)Faculty of Medicine at Memorial University of Newfoundland & Labrador,Department of Diagnostic Imaging at St. Clareu2019s Mercy HospitalLearning Objectives:1.tReview typical presentations of lymphoma2.tReview imaging features of lymphoma3.tReview four atypical presentations of lymphoma:a.tPrimary Bone Lymphomab.tPrimary Breast Lymphomac.tPrimary Pulmonary Lymphomad.tPrimary CNS LymphomaBackground:Lymphoma is typically identified by lymphadenopathy, constitutional symptoms, and, in a minority of cases, associated extranodal manifestations. Diagnosis is based on morphology, immunological markers, and cytogenetics from tissue biopsy, while staging relies mainly on PET/CT. It is not often that lymphoma is identified first by diagnostic imaging, without clinical suspicion. Moreover, primary lymphoma of the bone, breast, lung, and CNS are rare entities themselves. In this poster, we review each of these extranodal lymphomas through cases detected by imaging.Conclusion: When the typical clinical picture of lymphoma is not black and white, it is important to be aware of how one may present in the shades of grey of diagnostic imaging. In an atypical presentation marked by an isolated symptom or screening abnormality, radiologists may be the first to suggest a pathology like lymphoma. It becomes important, then, to have a raised index of suspicion for indolent or aggressive forms of lymphoma when interpreting images without a clinical history of constitutional symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.025 | 0.020 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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; both teacher heads agree on what is shown here.
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".