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Record W4394813756 · doi:10.36106/ijar/1802832

ROLE OF CROSS SECTIONAL IMAGING IN SENSORINEURAL HEARING LOSS

2024· article· en· W4394813756 on OpenAlexaff
Manu Gupta, Karan Jyani, Bhumika Luniwal, Heera Ram, Hemnat Kumar Mishra

Bibliographic record

VenueINDIAN JOURNAL OF APPLIED RESEARCH · 2024
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsRoyal Alberta Museum
Fundersnot available
KeywordsCross-sectional studyAudiologySensorineural hearing lossMedicineHearing lossPathology

Abstract

fetched live from OpenAlex

Purpose: Aims and objective of the study is to determine the incidence of structural cochlear anomalies in Sensorineural hearing loss and to evaluate them using HRCT and MRI Scan of temporal bone to asses feasibility of cochlear implantation. The study was carried out in the department of radiodiagnosis over a period of 16 mont Method: hs. The study included 114 patients of moderate to profound SNHL to evaluate further using HRCT and MRI Scan of temporal bone. Out of t Results: otal 114 pts in our study 75 pts had normal CT/MRI of temporal bone with no structural abnormalities detected and 39 number of patients had structural malformations which were feasible for cochlear implantation. Small percentage of patients (11%) with common cavity and absent cochlear nerve were not feasible for cochlear implantation. HRCT and MR imaging play an important role in evaluation of congenital heari Conclusion: ng loss by providing crucial information about the inner ear, vestibulocochlear nerve, and brain. Both modalities precisely and accurately delineate the inner ear anatomy and malformations preoperatively in cochlear implant patients.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.384
Teacher spread0.329 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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