From Microcytosis to Macrodiagnosis
Bibliographic record
Abstract
A 12-year-old Hispanic girl presented with fatigue, lightheadedness, and intermittent headaches. She was depressed and appeared pale to her mother. Her examination was unremarkable except for palpebral conjunctival pallor and was otherwise noncontributory. She had a profound hypoproliferative microcytic anemia with low iron level, low transferrin saturation, and a normal ferritin level. The patient experienced improvement in clinical symptoms following transfusion of packed red blood cells and oral iron therapy. At follow-up 2 months later, she presented with similar symptoms and persistent microcytic anemia with low iron levels. Her ferritin level was increased along with markedly elevated C-reactive protein and erythrocyte sedimentation rate. An oral iron challenge demonstrated lack of absorption, and hepcidin level was also significantly elevated. Thorough gastrointestinal and rheumatologic evaluations were performed to search for a source of inflammation. Key components of the patient's social history supplemented by serology, radiographic, and pathologic findings ultimately cinched an unexpected diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".