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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".