MétaCan
Menu
Back to cohort
Record W4212896496 · doi:10.3389/fnins.2022.786383

Middle Ear Surgeries for Chronic Otitis Media Improve Cognitive Functions and Quality of Life of Age-Related Hearing Loss Patients

2022· article· en· W4212896496 on OpenAlexaboutno aff
Juanjuan Gao, Junyan Chen, Jia Xu, Sichao Liang, Haijin Yi

Bibliographic record

VenueFrontiers in Neuroscience · 2022
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsAudiologyHearing lossQuality of life (healthcare)CognitionMedicineOtitisMiddle earCognitive declineChronic Suppurative Otitis MediaConductive hearing lossDementiaSurgeryPsychiatryInternal medicineDisease

Abstract

fetched live from OpenAlex

Age-related hearing loss (ARHL) may limit communication, which is closely associated with cognitive decline of the elderly and negatively affects their quality of life. In ARHL patients who suffer chronic otitis media (COM), hearing impairment may worsen and negatively affect the cognition and quality of life. It is currently unknown whether restoration of the conductive hearing in the mixed hearing loss through middle ear surgeries can improve both the cognitive function and quality of life of the ARHL patients. Therefore, in the present study, the ARHL patients were followed up for 6 months after middle ear surgeries for COM, and both the cognitive functions and quality of life of the patients were assessed using Montreal Cognitive Assessment and Glasgow Benefit Inventory. It was found that both the cognitive functions and quality of life were improved 6 months after middle ear surgeries. In conclusion, hearing recovery after middle ear surgeries could improve cognitive functions and quality of life of ARHL patients with COM, and surgical intervention is, hence, recommended for COM.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.051
GPT teacher head0.272
Teacher spread0.221 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in NeuroscienceSame topicHearing Loss and RehabilitationFrench-language works237,207