Risk Factors for Neonatal Clavicular Fractures
Bibliographic record
Abstract
BACKGROUND: Neonatal clavicular fractures represent the most common fracture during delivery. We aimed to define risk factors associated with these fractures in a large population-based database. METHODS: Data were extracted from Clalit Health Services' electronic health records from 2000 to 2020. Newborns with clavicular fractures were compared with a healthy control group. The following parameters were compared-for the newborns: sex, birth weight, birth height, and head circumference; for the delivery process: assisted delivery, cesarean section, use of epidural, birth week, and number of fetuses; and for the mother: age at delivery, socioeconomic status, height, weight, and body mass index (BMI). RESULTS: We found a rate of 0.28% for neonatal clavicular fractures (5015 clavicular fractures/1 755 660 deliveries). Male gender and heavier birth weight were found to be significantly associated with clavicular fractures ( P < .001). Increased risk was also associated with lower socioeconomic status, baseline weight, and maternal BMI ( P < .001 for all). Assisted delivery increased the risk of clavicular fracture (OR = 2.274; 95% CI, 1.661-3.115; P < .0001), while cesarean section and use of epidural were found to be protective (OR = 0.149; 95% CI, 0.086-0.26; P < .0001; and OR = 0.687; 95% CI, 0.0531-0.89; P < .004, respectively). CONCLUSIONS: This study provides insight into the risk factors associated with neonatal clavicular fractures on the largest group of patients reported to date.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".