Breast implant–associated anaplastic large cell lymphoma and effusions: A review with emphasis on the role of cytopathology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breast implants are surgically implanted by the hundreds of thousands every year worldwide for reconstructive or aesthetic purposes. Complications related to breast implants include early and late effusions that are often submitted for cytopathological analysis, particularly to exclude the possibility of breast implant-associated anaplastic large cell lymphoma (BIA-ALCL), a rare disease that generally follows an indolent clinical course, although it is becoming clearer that a subset of patients with adverse features have a poorer prognosis. Since a late-onset breast implant-associated effusion is the most common initial presentation of BIA-ALCL, cytopathological analysis of these effusions is considered the cornerstone and gold standard for rapid, efficient, reliable diagnosis and is critical for appropriate management and treatment. The National Comprehensive Cancer Network recently published clinical guidelines for the diagnosis and management of BIA-ALCL and stresses the essential role of cytopathological analysis, although it remains a matter of debate if all seromas should undergo immunocytochemistry or flow cytometry, particularly for assessment of expression of CD30 irrespective of morphological appearance on cytology. Herein, we review the current knowledge on BIA-ALCL, review the key cytological findings of reactive and malignant effusions related to breast implants, and present a comprehensive cytopathological workup with the presence of atypical cells as the key and pivotal element triggering further ancillary studies. We believe this approach will ensure appropriate and cost-effective management of effusion specimens from breast implants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it