Minor injuries… major implications: Watching out for sentinel injuries
Bibliographic record
Abstract
An otherwise healthy two-month-old boy presented to his general paediatrician for follow-up of gastroesophageal reflux disease. His mother reported that since starting ranitidine two weeks earlier, his discomfort following feeds and previously reported excessive crying had both improved. She had no new concerns about the child’s health but reported that she had observed a small amount of blood on the infant’s crib sheet in the past week, which she could not explain. On examination, he appeared alert with normal vital signs and had adequate weight gain since his last visit. Cardiovascular, respiratory, abdominal and neurological examinations were within normal limits. The paediatrician made note of a solitary, blue-green bruise on the baby’s left anterior thigh, which measured approximately 2 cm in diameter. He inquired about its origin; however, the child’s mother stated that she had not noticed it and was unsure of the causal mechanism. There were no other skin findings, bruises or petechiae noted. Examination of the oral cavity was unremarkable. The paediatrician prescribed a three-month supply of ranitidine and recommended routine follow-up with the family physician for well-baby care and immunizations.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".