Case 1: A long history of cough and dyspnea
Bibliographic record
Abstract
A 10-year-old girl presented with a nine-year history of six to eight upper and lower respiratory tract infections annually, each with a duration of three to six weeks in spite of antibiotic treatment, accompanied with cough, dyspnea and occasional wheezing. She also reported these symptoms during physical exercise, independent of infections. Until now, three pneumonias and multiple bronchitic episodes had been verified. At five years of age, she was diagnosed with bronchial asthma. For two years, treatment consisted of intermittent courses of short-acting beta-agonists and inhaled corticosteroids, before she was switched to a daily combination treatment with long-acting beta-agonists and inhaled corticosteroids (budesonide-formoterol 160 μg/4.5 μg twice daily) plus montelukast 5 mg orally without any breakthrough. A sweat test performed at four years of age was normal, as was a skin prick test to common aeroallergens and measurements of exhaled nitric oxide. Previous lung function tests were reported as a bronchial obstruction with no or only partial bronchodilator response. On presentation, the child was afebrile with normal vital signs, including an oxygen saturation of 97% on room air. A physical examination was unremarkable except for a prolonged expiration on auscultation. A complete blood count was normal and the erythrocyte sedimentation rate was 18 mm/h (0 mm/h to 10 mm/h). Further workup revealed the 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".