Current Risk Estimate of Breast Implant–Associated Anaplastic Large Cell Lymphoma in Textured Breast Implants
Bibliographic record
Abstract
BACKGROUND: With breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) now accepted as a unique (iatrogenic) subtype of ALCL directly associated with textured breast implants, we are now at a point where a sound epidemiologic profile and risk estimate are required. The aim of this article is to provide a comprehensive and up-to-date global review of the available epidemiologic data and literature relating to the incidence, risk, and prevalence of BIA-ALCL. METHODS: All current literature relating to the epidemiology of BIA-ALCL was reviewed. Barriers relating to sound epidemiologic study were identified, and trends relating to geographical distribution, prevalence of breast implants, and implant characteristics were analyzed. RESULTS: Significant barriers exist to the accurate estimate of both the number of women with implants (denominator) and the number of cases of BIA-ALCL (numerator), including poor registries, underreporting, lack of awareness, cosmetic tourism, and fear of litigation. The incidence and risk of BIA-ALCL have increased dramatically from initial reports of 1 per million to current estimates of 1/2,832, and is largely dependant on the "population" (implant type and characteristics) examined and increased awareness of the disease. CONCLUSIONS: Although many barriers stand in the way of calculating accurate estimates of the incidence and risk of developing BIA-ALCL, steady progress, international registries, and collegiality between research teams are for the first time allowing early estimates. Most striking is the exponential rise in incidence over the last decade, which can largely be explained by the increasingly specific implant subtypes examined-driven by our understanding of the pathologic mechanism of the disease. High-textured high-surface area implants (grade 4 surface) carry the highest risk of BIA-ALCL (1/2,832).
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".