Charcot–Marie–Tooth Disease and Hearing Loss: A Systematic Review With Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To characterize the pattern of hearing loss in Charcot-Marie-Tooth (CMT) disease to help guide clinical management. DATABASES REVIEWED: CINAHL, PubMed, and Scopus. METHODS: Two independent investigators selected studies on CMT patients with pure-tone average (PTA) and auditory brainstem response (ABR) data. Case reports, case series <5 patients, and data that overlapped with another study were excluded. Investigators performed data extraction, quality rating, and risk-of-bias assessment using the Newcastle-Ottawa Scale. Meta-analysis of mean difference using fixed/random effects models was used. Also, data were analyzed using a weighted one-way analysis of variance, with post-hoc Tukey's test for comparison. RESULTS: Ultimately, 6 prospective studies (N = 197) were included. The most common demyelinating subtype (CMT1A) had significantly prolonged ABR latency values across wave III (0.20 ms, 95% confidence interval [CI]: 0.05-0.35), wave V (0.20 ms, 95% CI: 0.01-0.39), waves I-III (0.20 ms, 95% CI: 0.01-0.39), and waves I-V (0.20 ms, 95% CI: 0.01-0.39) when compared to matched controls. The autosomal recessive demyelinating subtype (CMT4C) had significantly worse PTA when compared to the most common subtype (CMT1A) (Δ 28.93 dB, 95% CI 18.34-39.52) and nondemyelinating subtype (CMT2A) (Δ 28.3 dB, 95% CI: 15.98-40.62). CONCLUSIONS: Patients with CMT can present with a variety of phenotypes depending on the causative mutation. The ABR interpeak latency values for the most common demyelinating form of CMT are delayed when compared to matched controls. Most subtypes have normal hearing thresholds, apart from CMT4C, which presents with mild hearing loss on average.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".