Effect of Medial Olivocochlear Efferents on Speech Discrimination in Noise in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Patients with multiple sclerosis (MS) experience difficulties in understanding speech in noise despite having normal hearing. AIM: This study aimed to determine the relationship between speech discrimination in noise (SDN) and medial olivocochlear reflex levels and to compare MS patients with a control group. MATERIAL AND METHODS: Sixty participants with normal hearing, comprising 30 MS patients and 30 healthy controls, were included. For both groups, distortion product otoacoustic emissions (DPOAEs) were recorded at frequencies of 1000, 1400, 2000, 2800, 4000, 5600 and 8000 in the presence and absence of contralateral white sound at 65 dB SPL. Speech discrimination tests in the presence and absence of noise, Symbol Digit Modalities Test (SDMT) and Montreal Cognitive Assessment (MoCA) scale were applied to all participants to evaluate their cognitive skills. RESULTS: In age- and sex-matched groups, the DPOAE signal-to-noise ratio value was 6.50 ± 1.30 in the right ear at a frequency of 8000 Hz in the control group and 2.40 ± 1.75 in the MS group (P < 0.05). In the comparison of suppression between ears, lower suppression was found at 1400 and 2000 Hz in the left ear and 1000 Hz in the right ear in the MS group (P < 0.05). In the control group, a moderately significant positive correlation existed between right ear SDN scores and left ear suppression values (P < 0.05). The cognitive functions of the MS group were lower in MoCA and SDMT (P < 0.05). Patients who scored less than 21 points in MoCA also had low suppression results (P < 0.05). CONCLUSION: Comprehensive evaluations are necessary to uncover the presence of auditory perception disorders, such as noise sensitivity or speech disorders in noise, amongst MS patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".