Bibliographic record
Abstract
Case: You are a community paediatrician seeing a 28-day-old newborn for poor weight gain. At 14 days, she was slightly below birth weight, prompting additional assessment. A first-born child, she was breastfed and takes 400 International Units of vitamin D daily. A review of systems and physical examination did not provide additional clues. Perinatal history was uncomplicated, and the mother is a healthy 28-year-old. Due to your concerns, you order some investigations. These include normal serum sodium, potassium, chloride, albumin, creatinine, urea, and phosphorus. The total calcium is 2.70 mmol/L. The blood gas to ascertain acid-base status showed a normal pH, but the ionized calcium was 1.43 mmol/L. Urine analysis was normal. You suspect that she has hypercalcemia causing borderline weight gain. Hypercalcemia typically presents with non-specific findings, such as impaired feeding and poor weight gain. Since you believe typical total calcium concentrations in children are 2.2 to 2.58 mmol/L, a value of 2.70 mmol/L with ionized calcium at 1.43 mmol/L are both concerning.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".