Acquired cow’s milk sensitization after liver transplant in an adult: “clinical implications” and future strategies
Bibliographic record
Abstract
BACKGROUND: Identifying the mechanisms responsible for the development of food allergy in liver transplant recipients is more complex as there are several different clinical scenarios related to the immunological function of the liver. CASE PRESENTATION: We describe the first case of Transplant Acquired Food Allergy (TAFA) to cow milk in an adult following LT from a donor dead because of anaphylactic shock. A 67-year-old woman with primary biliary cirrhosis was referred to the Transplant Center of our hospital because of an acute-on-chronic liver failure. The donor was a 15-year-old girl deceased for anoxic encephalopathy due to food induced anaphylaxis after eating a biscuit. In the donor's history food allergies to cow milk and eggs were present. CONCLUSION: This case emphasizes the need for a standardized assessment of both solid-organ donors and recipients including donor allergy history in order to detect recipients at risk for anaphylaxis due to passive IgE transfer. Despite several reports of TAFA after solid organ, especially liver, an appropriate protocol to avoid risk for the recipient doesn't exist at the moment. The SPT (skin prick test) or specific IgE level are not enough to ensure a correct management in these cases and a correct education of the patients and the medical staff involved is absolutely necessary. It is the first case of milk allergy sensitization after solid organ transplant by passive transfer of IgE.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 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".