2022 Practice Recommendation Updates From the World Consensus Conference on BIA-ALCL
Bibliographic record
Abstract
BACKGROUND: Laboratory and clinical research on breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is rapidly evolving. Changes in standard of care and insights into best practice were recently presented at the 3rd World Consensus Conference on BIA-ALCL. OBJECTIVES: The authors sought to provide practice recommendations from a consensus of experts, supplemented with a literature review regarding epidemiology, etiology, pathogenesis, diagnosis, treatment, socio-psychological aspects, and international authority guidance. METHODS: A literature search of all manuscripts between 1997 and August 2021 for the above areas of BIA-ALCL was conducted with the PubMed database. Manuscripts in different languages, on non-human subjects, and/or discussing conditions separate from BIA-ALCL were excluded. The study was conducted employing the Delphi process, gathering 18 experts panelists and utilizing email-based questionnaires to record the level of agreement with each statement by applying a 5-point Likert Scale. Median response, interquartile range, and comments were employed to accept, reject, or revise each statement. RESULTS: The literature search initially yielded 764 manuscripts, of which 405 were discarded. From the remaining 359, only 218 were included in the review and utilized to prepare 36 statements subdivided into 5 sections. After 1 round, panelists agreed on all criteria. CONCLUSIONS: BIA-ALCL is uncommon and still largely underreported. Mandatory implant registries and actions by regulatory authorities are needed to better understand disease epidemiology and address initial lymphomagenesis and progression. Deviation from current diagnosis and treatment protocols can lead to disease recurrence, and research on breast implant risk factors provide insight to etiology.
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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 | 0.143 | 0.303 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.020 | 0.013 |
| Insufficient payload (model declined to judge) | 0.028 | 0.026 |
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".