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Record W4403617201 · doi:10.1017/s0022215124001397

Paediatric temporal bone fractures: a single centre experience

2024· article· en· W4403617201 on OpenAlexafffund
Arash Algouneh, Edward Madou, Karan Gandhi, Josée Paradis, M. Elise Graham, Julie E. Strychowsky, Murad Husein, Peng You

Bibliographic record

VenueThe Journal of Laryngology & Otology · 2024
Typearticle
Languageen
FieldMedicine
TopicFacial Nerve Paralysis Treatment and Research
Canadian institutionsLondon Health Sciences CentreWestern University
FundersLondon Health Sciences CentreLawson Health Research Institute
KeywordsTemporal boneMedicineDentistryGeologyComputer scienceSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to evaluate clinical characteristics, treatments and outcomes of paediatric temporal bone fractures at our institute. METHODS: A retrospective study of paediatric skull fractures confirmed by imaging from January 2010 to December 2022. Data on demographics, clinical presentations, injury mechanisms and complications were analysed, and fractures were categorised into otic capsule sparing (OCS) and violating (OCV). RESULTS: Of 369 skull fracture cases, 88 (24 per cent) involved temporal bones, predominantly caused by falls and vehicle accidents. Common symptoms were loss of consciousness, hematoma, and hemotympanum, with complications like facial nerve injury and cerebrospinal fluid leaks in 3.4 per cent of cases. OCV fractures led to more severe complications, including hearing loss. Audiology showed 65 per cent without hearing impairment, while others had various degrees of loss. CONCLUSION: Paediatric temporal bone fractures, particularly OCV types, pose significant challenges. Early detection and thorough management are vital, underscoring the need for consistent data collection and regular audiometric monitoring.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.023
GPT teacher head0.332
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes2
Has abstractyes

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Same venueThe Journal of Laryngology & OtologySame topicFacial Nerve Paralysis Treatment and ResearchFrench-language works237,207