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Record W2437002559 · doi:10.1017/s0022215116008197

Cochlear implantation in elderly patients: stability of outcome over time

2016· article· en· W2437002559 on OpenAlexaff
Ohad Hilly, Euna Hwang, L. Smith, David Shipp, Julian M. Nedzelski, Jenny Chen, Vincent Lin

Bibliographic record

VenueThe Journal of Laryngology & Otology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineCochlear implantationQuality of life (healthcare)Cochlear implantRetrospective cohort studyHearing lossImplantAudiologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Cochlear implantation is the standard of care for treating severe to profound hearing loss in all age groups. There is limited data on long-term results in elderly implantees and the effect of ageing on outcomes. This study compared the stability of cochlear implantation outcome in elderly and younger patients. METHODS: A retrospective chart review of cochlear implant patients with a minimum follow up of five years was conducted. RESULTS: The study included 87 patients with a mean follow up of 6.8 years. Of these, 22 patients were older than 70 years at the time of implantation. Hearing in Noise Test scores at one year after implantation were worse in the elderly: 85.3 (aged under 61 years), 80.5 (61-70 years) and 73.6 (aged over 70 years; p = 0.039). The respective scores at the last follow up were 84.8, 85.1 and 76.5 (p = 0.054). Most patients had a stable outcome during follow up. Of the elderly patients, 13.6 per cent improved and none had a reduction in score of more than 20 per cent. Similar to younger patients, elderly patients had improved Short Form 36 Health Survey scores during follow up. CONCLUSION: Cochlear implantation improves both audiometric outcome and quality of life in elderly patients. These benefits are stable over time.

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)0.002
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 score0.442
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.025
GPT teacher head0.293
Teacher spread0.269 · 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

Citations42
Published2016
Admission routes1
Has abstractyes

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