Pediatric temporal bone fractures: A systematic review
Bibliographic record
Abstract
Pediatric temporal bone fractures, pose significant risks including hearing loss, facial nerve paralysis, and intracranial complications. This study aims to address the knowledge gap in outcomes following these fractures in children. A comprehensive literature search across Embase, MEDLINE (PubMed), and Web of Science was conducted following PRISMA guidelines. The primary outcome analyzed was hearing loss, with secondary outcomes including facial nerve injury, and other complications. This study included 15 articles with outcomes for 1044 patients. The risk of sensorineural hearing loss (SNHL) was higher in otic capsule-violating (OCV) fractures than otic capsule-sparing (OCS) fractures (OR 28.57, p < 0.001). Facial nerve injury was more likely in OCV fractures (OR 4.59, p = 0.0162). Transverse fractures had higher odds of SNHL compared to longitudinal fractures (OR 5.181, p < 0.001). OCV fractures had higher odds of facial nerve injury compared to OCS fractures (OR 4.59, p = 0.0162), and transverse fractures had higher odds of facial nerve injury compared to longitudinal fractures (OR 3.02, p = 0.0146). No significant differences in conductive hearing loss were found between fracture types. Only 57 % of patients had audiometric data available. This study indicates no single classification system accurately predicts outcomes for all pediatric temporal bone fractures. OCV fractures do carry a higher risk of SNHL than OCS fractures. However, this should not replace a thorough clinical assessment and audiometric testing. Long-term studies are needed to improve patient care due to limited data on long-term effects.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".