Case 4: Poor Feeding and Lethargy in a 32-day-old Infant
Bibliographic record
Abstract
1. Erin R. Peebles, MD, FRCPC* 2. Tamara A. VanHooren, MD, FRCPC* 3. Anna C. Gunz, MD, FRCPC* 4. Marina I. Salvadori, MD, FRCPC* 1. *Western University, London, Ontario, Canada A 32-day-old boy presents to the emergency department with a 12-hour history of poor feeding and lethargy. The child was born at 34+1 weeks and spent 2 weeks in the NICU, where he was fed infant ready-made formula by gavage feeding as he gradually increased his suckling. He was discharged 5 days before his presentation. On assessment, he is noted to be pale, hypotonic, and irritable. He is hypothermic, with a rectal temperature of 97.0°F (36.1°C). His heart rate is 150 beats/min, and capillary refill is noted to be appropriate. A full sepsis evaluation is performed. White blood cell count is 20,000/μL (20×109/L), hemoglobin is 9.3 g/dL (93 g/L), and platelet count is 63×103/μL (63×109/L). Cerebrospinal fluid analysis shows a white blood cell count of 10,000/μL (10×109/L), a protein level of 1,650 g/dL (16,500 g/L), and a glucose level of 5.6 mg/dL (0.31 mmol/L). He is started on ampicillin, cefotaxime, and acyclovir. Within 5 hours of admission, he becomes mottled and tachycardic (190 beats/min), with intermittent apneas requiring PICU admission. Gram-negative bacilli are identified on gram stain in the cerebrospinal fluid (CSF) 6 hours after presentation; the acyclovir is stopped, and the …
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| 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".