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Record W4411722452 · doi:10.1007/s11357-025-01742-2

International Consortium on Ageing-Related Pathologies (ICCARP) Audiovestibular Group: fostering international consensus to refine International Classification of Diseases (ICD-11) codes for hearing loss across the life course

2025· review· en· W4411722452 on OpenAlexaff
Dialechti Tsimpida, Michael A. Akeroyd, Barry L. Bentley, Michael R. Bowl, Emma Broome, Stuart Calimport, Sian Calvert, Gary Christopher, Tom Dening, Silvia Di‐Bonaventura, A.K. Goswami, Spyridon Gougousis, Paul Govaerts, Helen Henshaw, Robert Huckstepp, Vasiliki Iliadou, Theano K Koutsimani, Morag A. Lewis, Frank R. Lin, Cecilia Luisa Miotto, Lisa S. Nolan, Helen E. Nuttall, Chukwuebuka Prince Onyekere, Mukovhe Phanguphangu, Christopher J. Plack, Rohini Raghavan, Nicholas S. Reed, Konstantina Rova, Karen P. Steel, Robert J. Stokroos, De Wet Swanepoel, Agnieszka J. Szczepek, Susan L. Whitney

Bibliographic record

VenueGeroScience · 2025
Typereview
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsYork University
FundersNIHR Nottingham Biomedical Research CentreBiotechnology and Biological Sciences Research CouncilMedical Research CouncilManchester Biomedical Research Centre
KeywordsHearing lossAudiologyAgeingConsensus conferenceMedicinePsychologyInternal medicine

Abstract

fetched live from OpenAlex

Following the World Health Organization's (WHO's) decision to classify age-related aetiologies [1], and a global call for action to systematically classify the pathologies of ageing [2], the International Consortium to Classify Ageing-Related Pathologies (ICCARP) was established in 2023 under the leadership of Cardiff Metropolitan University [3,4].Within this consortium, the Audiovestibular Group is actively working to refine the classification of hearing and balance disorders, aligning with the WHO's Co-authors listed in alphabetical order.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.142
GPT teacher head0.404
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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