Diagnostic difficulties of Addison's disease in children: a case report
Bibliographic record
Abstract
Addison’s disease is a rare chronic endocrine disorder resulting from primary adrenal insufficiency. Symptoms are non-specific and can arise insidiously, including asthenia, muscular weakness, weight loss, irritability, depression, loss of appetite, dyspepsia, nausea and vomiting. A peculiar clinical sign of Addison’s disease is hyperpigmentation of skin and mucous membrane due to overproduction of pro-opiomelanocortin (POMC), precursor of ACTH and MSH (melanocyte stimulating hormone). Without therapy or in the course of triggering events such as infections, surgery or trauma the presentation can be dramatic and may cause an adrenal crisis, a potentially lethal medical emergency. Biochemical investigations are essential for diagnosis and commonly reveals electrolyte abnormalities (hyponatraemia and hyperkalaemia), hypoglycaemia, reduced cortisol levels and increased levels of ACTH. The lack of response to ACTH stimulation test confirms the primary adrenal insufficiency. Treatment for Addison's disease is based on replacing missing cortisol, the most commonly used drug is hydrocortisone often in association with fludrocortisone as replacement for the missing aldosterone. Due to its non-specific presentation, identification of this condition is difficult and can be confused with other disorders, for example eating disorders. We report a case of a young boy with Addison’s disease mistaken for anorexia nervosa.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| 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".