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Record W4413816581 · doi:10.1097/nm9.0000000000000045

Critical Insights From FDG PET CT in Diagnosing Delayed Breast Implant-Associated Anaplastic Large Cell Lymphoma Following Textured Implant Recall

2025· article· en· W4413816581 on OpenAlexaff
Shahad Howladar, Alireza Khatami

Bibliographic record

VenueClinical nuclear medicine open. · 2025
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsAnaplastic large-cell lymphomaImplantBreast implantMedicineLymphomaRadiologyPositron Emission Tomography-Computed TomographyPositron emission tomographyNuclear medicinePathologySurgery

Abstract

fetched live from OpenAlex

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a rare T-cell lymphoma strongly associated with textured breast implants. This study presents a case of breast implant-associated anaplastic large cell lymphoma in 58-years-old female patient presented with asymmetrical right breast enlargement after 14 years of texture implant recall. MRI of the right breast showed mild thickening of the fibrous capsule surrounding the implant with diffuse asymmetric enhancement. PET-CT demonstrated a small right peri-implant fluid collection with mild FDG uptake in the superior aspect and a mildly hypermetabolic right axillary lymph node. The breast ultrasound revered fluid collection, and 50 mL of straw-colored fluid was aspirated. Cytology of the fluid revealed abnormal cells positive for CD45, CD30, CD3, but negative for CD20. The Ki-67 proliferation index was 20%, and ALK staining was negative indicating anaplastic large cell lymphoma (BIA-ALCL).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.021
GPT teacher head0.339
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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