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Record W2897034316 · doi:10.1155/2018/5463753

Three-Dimensional Changes of the Auditory Canal in a Three-Year Period during Adolescence Using CBCTs

2018· article· en· W2897034316 on OpenAlexaff
Adam Woods, Manuel O. Lagravère

Bibliographic record

VenueInternational Journal of Dentistry · 2018
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPeriod (music)AudiologyAuditory canalMedicineAcousticsPhysicsAnatomy

Abstract

fetched live from OpenAlex

PURPOSE: There is a lack of identifying suitable regions in the head that can be used for three-dimensional superimposition techniques. For this reason, with the use of cone-beam computed tomography (CBCT), the ear canals were analyzed to verify changes during a period of three years in the adolescent years. METHODS: CBCTs from fifty-six patients (ages: 10 to 20) were used to landmark the anatomy of the ear canals. Each patient was analyzed using two CBCT reconstructions that were taken approximately three years apart. AVIZO® software was used to locate 28 landmarks distributed following the ear canal path and foramina (ovale, spinosum, rotundum, etc.) in the cranial base to obtain spatial relationships. Three-dimensional coordinates were obtained from the landmarks, and the average distance between various landmark pairings was calculated. The repeated measure ANCOVA was used to determine statistical significance. RESULTS: In the main data set, the largest mean distance change was found to be 4.37 mm ± 18.29 mm between the left foramen ovale and the left superior medial ear canal opening. The smallest mean distance change was 0.18 mm ± 3.25 mm between the right inferior lateral ear canal opening and the right inferior medial ear canal opening. CONCLUSIONS: During the adolescent years, the ear canal presents dimensional changes. Even though in different areas throughout the canal, the average distances were minor, still, large standard deviations were present; thus, caution should be taken when trying to use this structure for superimposition of CBCTs.

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)0.000
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 score0.031
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.022
GPT teacher head0.287
Teacher spread0.265 · 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
Published2018
Admission routes1
Has abstractyes

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