Clinical-radiological features of fractures in premature infants – a review
Bibliographic record
Abstract
Premature infants are more vulnerable to bone fractures than term infants for numerous reasons, directly or indirectly related to prematurity. Although the reported incidence of fractures in this vulnerable population is somewhat inconsistent, the increased risk is clear. Metabolic disorders, genetic disease, accidental trauma, and non-accidental injury can all account for fractures in premature infants, so that determining the etiology is of importance. This increased risk does not appear to continue into childhood. Thus, most of these fractures would be found in children <3 years of age, often within the first year of life. Unfortunately, this is the same age group in which the majority of non-accidental injury (NAI) cases, frequently presenting with fractures, are seen. Further confounding the diagnosis is the possibility of previously undiagnosed fractures from trauma during delivery, and fractures due to bone weakening by metabolic diseases. A multi-dimensional approach using a combination of diagnostic procedures is necessary to properly identify the location of the fractures, the bone structure and characteristics, and the history with regards to family situation and medical treatment. This paper reviews the potential factors related to fractures in premature infants and the differential diagnoses of child abuse fractures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".