Bibliographic record
Abstract
Child abuse is the third leading cause of death in children between one and four years of age, and almost 20% of child homicide victims have contact with a health care professional within a month of their death. Therefore, family physicians are in an ideal position to detect and intervene in cases of suspected child maltreatment. There is currently insufficient evidence that screening parents or guardians for child abuse reduces disability or premature death. Assessment for physical abuse involves evaluation of historical information and physical examination findings, as well as radiographic and laboratory studies, if indicated. The history should be obtained in a nonaccusatory manner and should include details of any injuries or incidents, the patient's medical and social history, and information from witnesses. The physical examination should focus on bruising patterns, injuries or findings concerning for abuse, and palpation for tenderness or other evidence of occult injury. Skeletal survey imaging is indicated for suspected abuse in children younger than two years. Imaging may be indicated for children two to five years of age if abuse is strongly suspected. Detailed documentation is crucial, and includes photographing physical examination findings. Physicians are mandated by law to report child abuse to the local child protective services or law enforcement agency. After a report is made, the child protection process is initiated, which involves a multidisciplinary team approach.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.011 | 0.004 |
| Insufficient payload (model declined to judge) | 0.990 | 0.992 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".