A Case of Angioimmunoblastic T-cell Lymphoma Hidden in Plain Sight: A Delay in Diagnosis and Management
Bibliographic record
Abstract
Angioimmunoblastic T-cell lymphoma (AITL) is an uncommon type of cluster of differentiation (CD)4 T-cell peripheral lymphoma. The varied clinical presentations of AITL present a challenge for accurate diagnosis. We present a case of a 57-year-old female with a history of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in May 2020, who presented to the hospital in the summer of 2021 with shortness of breath for 3 months. She underwent an extensive workup for lymphadenopathy while in Canada involving multiple core lymph node biopsies, which were inconclusive. Here in our institution, several tests for infectious diseases were unremarkable. Imaging tests revealed bilateral pleural effusion, lymphadenopathies, and rectal thickening. Results from rectal biopsy and excisional cervical lymph node biopsy revealed findings typical of AITL. Due to worsening hypoxia with pleural fluid accumulation, bilateral chest tubes (PleurX catheter) were placed. Steroids and chemotherapy were started. She was discharged in stable condition to follow-up care. An integrated and persistent approach comprising clinical, morphologic, excisional biopsy, immunophenotyping, and molecular tests is essential in reaching a correct diagnosis of AITL. Through our consistent effort to obtain further imaging and tissue biopsies, we came to the diagnosis which allowed her to begin appropriate life-saving treatments.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".