Case 1: The case of the irritable nephrotic
Bibliographic record
Abstract
A 22-month-old previously healthy boy presented to a tertiary care emergency department with a one-week history of periorbital and bilateral leg swelling. He had no fever, no recent illnesses and no blood in his urine or stool. He was known to have chronic constipation and had a poor diet consisting mainly of cow's milk. On physical examination, he had no hypertension, was in no apparent distress and displayed normal vital signs. He had marked periorbital edema and pitting edema of his lower limbs. His abdomen was distended and he had mild ascites. The rest of his examination was normal. A urine dipstick showed a high protein level (20 g/L) and a large amount of blood. He had low serum albumin of 18 g/L (normal 32 g/L to 56 g/L), high serum triglycerides of 2.17 g/L (normal 0.31 g/L to 1.41 g/L) and high serum cholesterol of 8.82 mmol/L (normal 3.2 mmol/L to 4.4 mmol/L). His electrolytes and renal function were normal. A complete blood count revealed a low hemoglobin of 79 g/L (normal 110 g/L to 140 g/L) with a low mean corpuscular volume of 53 fL (normal 80 fL to 94 fL). His platelet count was elevated at 934×109/L (normal 150×109/L to 400×109/L). A blood film showed marked hypochromasia and microcytosis. His iron and ferritin levels were both low.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".