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Record W4409040318 · doi:10.1016/j.yjpso.2025.100208

Pediatric temporal bone fractures: A systematic review

2025· review· en· W4409040318 on OpenAlexaff
Karan Gandhi, Chloe Pulver, Peng You

Bibliographic record

VenueJournal of Pediatric Surgery Open · 2025
Typereview
Languageen
FieldMedicine
TopicFacial Nerve Paralysis Treatment and Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.175
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0140.005
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.090
GPT teacher head0.436
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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