Characteristics of Graves’ Disease in Haemodialysis Patients
Bibliographic record
Abstract
Abnormal thyroid hormone production and metabolism are relatively common in chronic renal failure and in regular haemodialysis. Chronic kidney disease is associated with decreased thyroid hormone concentrations, especially triiodothyronine (T3), which are referred to as the euthyroid sick syndrome associated with increased severity of non-thyroidal illness and mortality in cats and dogs. Hyperthyroidism is a very unusual condition in patients undergoing regular haemodialysis. Graves’ disease is rare in these patients. To our knowledge, till now only 8 well documented cases of Graves’ disease have been reported in patients undergoing regular haemodialysis. The diagonsis of Graves’ disease must be evoked in presence or even in the absence of specific symptoms of the disease in haemodialysis patients. Diagnosis of hyperthyroidism may be difficult because of similar signs and symptoms as in uremia and manifestions are inhabitual. Indeed, hypertension, gynaecomastia, anaemia and hypercalemia can be seen in the two pathologies. Amost all patients undergoing regular haemodialysis received iodine 131 therapy for the treatment of Graves’ disease. This treatment is efficient and safe. Isolation of the patient is not recommanded. The risk for dialysis staff is to be contaminated by an accidental ingestion of a biologic fluid from the patient. The usual protection barriers used during the haemodialysis session are sufficient. J Endocrinol Metab. 2012;2(6):212-215 doi: https://doi.org/10.4021/jem138e
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".