Thrombotic Microangiopathy After Long-Lasting Treatment by Gemcitabine: Description, Evolution and Treatment of a Rare Case
Bibliographic record
Abstract
Thrombotic microangiopathy (TMA) is an uncommon but severe complication that may occur in cancer patients under gemcitabine chemotherapy. Gemcitabine-induced thrombotic microangiopathy (G-TMA) can clinically and biologically present as atypical hemolytic uremic syndrome, with activation of the complement pathway asking the question of the use of eculizumab. We describe here the case of a patient suffering from metastatic cholangiocarcinoma treated by gemcitabine for 4 years leading to the remission of the underlying neoplasia. Despite an impressive response to therapy, she developed thrombopenia, regenerative anemia, and acute kidney injury leading to the suspicion then diagnosis based on the renal biopsy of a very late G-TMA. Spontaneous evolution after treatment interruption was favorable without dialysis requirement. However, in this case where gemcitabine is the only chemotherapy remaining for a mortal underlying condition, we discussed the re-initiation of gemcitabine under eculizumab treatment. This atypical case of TMA illustrates the importance of recognizing, even belatedly, this rare but serious complication of chemotherapy. It asks the question of rechallenging discontinued chemotherapy notably under eculizumab cover, in this population with a high risk of cancer progression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".