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Record W2419121928

Penetrating and blast ear trauma: 7-year review of two pediatric practices.

2008· article· en· W2419121928 on OpenAlexaff
Paul Mick, Paul Moxham, Jeffrey P. Lüdemann

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldMedicine
TopicTraumatic Ocular and Foreign Body Injuries
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicineAudiogramSurgeryHearing lossPediatric traumaVertigoWeaknessFacial weaknessRetrospective cohort studyNystagmusAudiologyPoison controlInjury preventionEmergency medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To review our experience with ear trauma in children. DESIGN/METHODS: Retrospective review of two practices from 2000 to 2007. SETTING: Pediatric tertiary care hospital. MAIN OUTCOME MEASURES: Micro-otoscopic findings and audiologic data. RESULTS: There were 18 cases of penetrating ear trauma (PET) and 6 cases of blast ear trauma (BET). The average age of the children with PET was 5 years. Fifteen of the 18 cases involved cotton-tipped applicators (CTAs); 8 patients had tympanic membrane perforations from CTA use. Six of the perforations healed spontaneously, and the other two patients were lost to follow-up. For 15 of the 18 PET patients, audiograms were available, and all eventually returned to normal. The average age of the six patients with BET was 11 years. BET caused five tympanic membrane perforations, all of which healed spontaneously. Three patients had audiograms, which were normal. The other three were lost to follow-up. None of the patients had vertigo, nystagmus, facial weakness, or cerebrospinal fluid otorrhea; this factored into our nonsurgical approach. CONCLUSIONS: PET and BET in children are underreported. PET usually involves CTA and occurs in younger children than does BET. Urgent surgical intervention is not indicated unless a child presents with neuro-otologic signs or symptoms. CTA avoidance should be taught systematically to the public.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.289
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2008
Admission routes1
Has abstractyes

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