Lymphocytic panniculitis: an algorithmic approach to lymphocytes in subcutaneous tissue
Bibliographic record
Abstract
The diagnosis of panniculitis is a relatively rare occurrence for many practising pathologists. The smaller subset of lymphocyte-predominant panniculitis is further complicated by the diagnostic consideration of T cell lymphoma involving the subcutaneous tissue, mimicking inflammatory causes of panniculitis. Accurate classification of the panniculitis is crucial to direct clinical management as treatment options may vary from non-medical therapy to immunosuppressive agents to aggressive chemotherapy. Many diseases show significant overlap in clinical and histological features, making the process of determining a specific diagnosis very challenging. However, with an adequate biopsy including skin and deep subcutaneous tissue, a collaborative effort between clinician and pathologist can often lead to a specific diagnosis. This review provides an algorithmic approach to the diagnosis of lymphocyte-predominant panniculitis, including entities of septal-predominant pattern panniculitis (erythema nodosum, deep necrobiosis lipoidica, morphea profunda and sclerosing panniculitis) and lobular-predominant pattern panniculitis (lupus erythematous panniculitis/lupus profundus, subcutaneous panniculitis-like T cell lymphoma, cutaneous γ-δ T cell lymphoma, Borrelia infection and cold panniculitis).
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".