Case 2: A pale infant – not a typical case of iron deficiency
Bibliographic record
Abstract
A 15-month-old boy presented to his doctor when his mother became concerned about his diet. Despite the introduction of solids at six months of age, he was almost exclusively breastfed and a ‘picky eater’. His parents were partial vegetarians, with little meat intake. His mother had noticed that he was pale with gait disturbance and occasional falls. His developmental history was otherwise unremarkable. His past medical history included a term delivery with no complications and an uneventful postnatal period. After initial bloodwork, he was referred to a paediatric hematologist. On examination, he was pale. His weight and height were between the 5th to 10th and 15th percentiles, respectively. A systolic murmur grade 2/6 was present. The patient's bloodwork included a complete blood count with white blood cell count 5.2×109/L (normal 5.3×109/L to 16×109/L), neutrophils 1×109/L (normal 1×109/L to 6.5×109/L), hemoglobin 83 g/L (normal 103 g/L to 135 g/L), mean corpuscular volume 107 fL (normal 70 fL to 86 fL) and platelet count 311×109/L (normal 200×109/L to 550×109/L). His reticulocyte count was 31×109/L (normal 40×109/L to 120×109/L), and his peripheral blood smear showed occasional oval macrocytes and hypersegmented neutrophils. A bone marrow aspirate was performed, and the images demonstrated classic findings of a particular diagnosis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".