Bibliographic record
Abstract
Since 1997, reports from the scientific community have suggested a possible association, without causation, between breast implants and anaplastic large cell lymphoma (ALCL). Analysis of these patients has been challenging. Many studies have been under-reported while others have been duplicated. In 2011, a United States Food & Drug Administration (FDA) ‘white paper’ analyzed 34 of the 60 cases reported worldwide. All 34 patients had undergone secondary surgery for breast swelling, firmness or pain. ALCL was an incidental finding. Diagnosis of ALCL is made by hematoxylin and eosin histology and immunochemistry for the CD30 marker. ALCL occurred with all types of implants. Subsequent studies have suggested that textured implants may have a greater risk. In all cases, ALCL cells were found in the capsule, in the seroma or within a mass adjacent to the implant. There was no invasion of cells beyond the capsule into the breast parenchyma. From the FDA study, the risk of developing ALCL after receiving implants appears to be approximately one in one million per year. All cases appear to be negative for the anaplastic lymphoma kinase marker. ALCL in most of these patients may represent a new entity with less aggressive behaviour. In most patients with capsule-confined disease, proper management may prove to be implant removal and capsulectomy. Patients with a distinct mass adjacent to their implant may have a more aggressive clinical course that may become systemic. They may require chemotherapy in addition to implant removal and capsulectomy. All cases of ALCL should be referred to an appropriate specialist and reported to the FDA.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".