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Record W17701976

Auditory symptoms in whiplash patients - could earwax occlusion be a benign cause?

2006· article· en· W17701976 on OpenAlexaff
Robert Ferrari

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTinnitusMedicineOcclusionAudiologyHearing lossSurgery
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Excess cerumen (earwax) in the external auditory canal is associated with symptoms of earache, fullness in the ears, and diminished hearing. These symptoms, and tinnitus, are commonly associated with whiplash injury. METHODS: Eighty-six whiplash patients were examined to determine if there was a correlation between symptoms of earache, fullness in the ear, diminished hearing, and tinnitus, and the degree of cerumen occlusion. Cerumen occlusion was measured by visualisation of the tympanic membrane and graded according to a 4 point scale. RESULTS: Of 71 subjects reporting no acute onset (within 7 days of the collision that caused their whiplash) earache, fullness in the ears, hearing loss, or tinnitus, 62 had little or no cerenum occlusion. Of seven subjects reporting tinnitus but no other auditory symptoms, none had greater than moderate cerenum occlusion. Of eight subjects reporting one or more of acute onset earache, fullness in the ears, diminished hearing, and tinnitus, seven had complete cerenum occlusion in the affected ear. DISCUSSION: The findings suggest high grade cerumen occlusion frequently occurs in the ear affected by acute auditory symptoms. However, tinnitus alone has no apparent association with cerumen occlusion. It is possible that a significant number of acute onset auditory symptoms reported in whiplash patients have a benign cause.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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 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

Citations7
Published2006
Admission routes1
Has abstractyes

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