Immunosuppressive Therapeutic Complications in a Lung Transplant Patient
Bibliographic record
Abstract
The number of lung transplants has increased in the last years. Besides, the complications of immunosuppressive treatment, such as infections, have also been raising. Another complication, although seldom described, is thrombotic microangiopathy associated with calcineurin inhibitors. We report a case of a 65-year-old woman with a 6-month bi-pulmonary transplant, admitted for pneumonia. Addressing the differential diagnosis, pneumonia in immunosuppressed patient, aspergillosis, CMV disease and acute rejection were considered. High levels of tacrolimus were identified. The patient evolved with multiple organ dysfunctions. Hemodialysis was started on the third day. On the 5th day, patient developed progressive anemia, leukopenia and thrombocytopenia, schistocytes were present on peripheral smear and Coombs test was negative. A thrombotic microangiopathy probably related to tacrolimus high levels was identified. Plasma exchange was initiated on the eighth day, and intensified on the 11th day, without success, and on 14th day, the patient died. We believe our case report is relevant, since thrombotic microangiopathy in lung transplant has rarely been described and lung transplant is a growing reality, as well as its complications. Furthermore, our case is more severe than the ones described in literature, possibly due to prolonged tacrolimus toxic levels and concomitant infection. J Med Cases. 2015;6(12):596-599 doi: http://dx.doi.org/10.14740/jmc2373w
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".