Bibliographic record
Abstract
A 22 month old white Australian boy presented to his general practitioner with irritability, red eyes, and a two day history of high fever (highest at 41°C) that did not respond to paracetamol and ibuprofen. He had no history of cough, coryza, or rash. His medical history was unremarkable and immunisations were up to date. On examination, he had bilateral non-exudative conjunctival injection and was diagnosed with a non-specific febrile illness with associated conjunctivitis. His family then took him on a planned family holiday to Thailand where his conjunctivitis improved, but the fevers continued unabated. In addition to the fevers, his parents noted a mild nappy rash. On day 8 of the illness, his parents took him to a local clinic after a couple of episodes of diarrhoea. He was diagnosed with gastroenteritis. Later that day he became more unwell with increasing irritability and a progressive rash so his parents took him to hospital. He was dehydrated and lethargic and physical examination showed cervical lymphadenopathy, fissured lips, and an injected pharynx. His hands and feet were oedematous and his legs were covered in a pink maculopapular rash. Blood tests showed leucocytosis and increased acute phase reactants, but other haematological markers were normal. A chest radiograph was normal. He was admitted with presumed bacterial sepsis and started on broad spectrum intravenous antibiotics. However, after two days of antibiotics, his symptoms did not improve. ### 1 What is the most likely diagnosis in this case? #### Short answer Kawasaki disease. #### Long answer The most likely diagnosis is Kawasaki disease. Differential diagnoses to be considered in a child with a history of …
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".