New insights into breast implant-associated anaplastic large cell lymphoma
Bibliographic record
Abstract
PURPOSE OF REVIEW: Breast implant-associated anaplastic large cell lymphoma (BI-ALCL) is a rare form of lymphoma arising adjacent to a breast implant. We aim to review the pathogenesis and clinico-biological features of BI-ALCL. RECENT FINDINGS: BI-ALCL is a new provisional entity in the 2017 updated WHO classification. Among several hypotheses, BI-ALCL development seems to be determined by the interaction of immune response related to implant products and additional genetic events. SUMMARY: BI-ALCL is an uncommon T-cell lymphoma which is increasingly diagnosed since its first description in 1997 with 500 estimated cases worldwide. Two BI-ALCL subtypes correlating with clinical presentation have been described. Although most BI-ALCL patients with tumor cell proliferation restricted to the periprosthetic effusion and capsule have excellent outcomes, other patients presenting with a tumor mass, may have a more aggressive disease. The pathogenesis of BI-ALCL remains elusive. It is postulated that local chronic inflammation elicitated by bacterial infection or implant products may promote the activation and proliferation of T cells. Additional genetic events resulting in the activation JAK/STAT pathway are also incriminated. Further investigations are needed to better characterize the pathogenesis of this disease in order to determine the potential risk to develop BI-ALCL after surgical implants.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".