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Record W2479358111 · doi:10.1177/0003489416660111

Depth of Cochlear Implant Array Within the Cochlea and Performance Outcome

2016· article· en· W2479358111 on OpenAlexaff
Ohad Hilly, Leah Smith, Euna Hwang, David Shipp, Sean Symons, Julian M. Nedzelski, Joseph M. Chen, Vincent Lin

Bibliographic record

VenueAnnals of Otology Rhinology & Laryngology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsCochlear implantationCochlear implantRound windowCochleaMedicineImplantHearing lossAudiologyElectrode arrayNuclear medicineElectrodeSurgeryChemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate whether the depth of cochlear implant array within the cochlea affects performance outcomes 1 year following cochlear implantation. METHODS: A retrospective case review of 120 patients who were implanted with the Advanced Bionics HiFocus 1J. Post-implantation plain-radiographs were retrospectively reviewed, and the depth of insertion was measured in degrees from the round window to the electrode tip. Correlation between the depth of insertion and 1-year post-activation Hearing in Noise Test (HINT) scores was analyzed. Intrascala position was not assessed. RESULTS: Depth of electrode insertion ranged from 180° to 720°, and HINT scores ranged from 0% to 100%. A Mann-Whitney U test demonstrated significantly improved 1-year post-activation HINT scores in patients with an insertion depth of 360° or more in comparison with patients with insertion depth of less than 360° (81% vs 61%, P = .048). Patients with 13 to 15 contacts within cochlear turns performed as well as patients with full insertion of all 16 contacts, while patients with only 12 contacts performed poorly. CONCLUSIONS: Insertion depth of the AB HiFocus 1J electrode of less than 360° is associated with reduced 1-year post-activation HINT scores when compared with deeper insertions. Partial insertion of 13 active contacts or more led to similar results as full insertion.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
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 head0.079
GPT teacher head0.329
Teacher spread0.250 · 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 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

Citations37
Published2016
Admission routes1
Has abstractyes

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