Click SP/AP Area Ratio Vesrus Tone Burst SP Amplitude to Diagnose Ménière's Disease Using Electrocochleography
Bibliographic record
Abstract
OBJECTIVES: To evaluate the sensitivity and the specificity of summating potential (SP)/action potential (AP) area under the curve (AUC) ratio by a transtympanic electrode and a click stimulus (TT-CS), SP/AP AUC ratio by an extratympanic electrode and a click stimulus (ET-CS) and SP amplitude value by a transtympanic electrode and tone burst stimulus (TT-TBS) in regard of Ménière's disease (MD) diagnosis. This is the first study that compares SP amplitude value performed by a TT-TBS and the SP/AP AUC ratio performed by a TT-CS. STUDY DESIGN: Retrospective comparative study. SETTINGS: Ninety-five patients met the inclusion criteria for electrocochleography (ECochG) testing in a tertiary care center. METHODS: The sensitivity and specificity of our different ECochG protocols were calculated in regard of the diagnosis of MD. RESULTS: The patients' mean age was 54 years old (female predominance). The sensitivity and the specificity of SP/AP area ratio by a TT-CS were 88.5% and 70.0%, respectively. On the other hand, the sensitivity and specificity for the SP amplitude value by a TT-TBS were 60.0% and 55.6%, respectively. SP/AP area ratio by TT-CS was statistically better than SP amplitude value by TT-TBS to detect MD disease (P = .016). However, no difference was identified between SP/AP area ratio by ET-CS and SP amplitude value by a TT-TBS (P = .573). CONCLUSION: SP/AP area ratio by click stimulation has higher sensitivity and specificity to detect MD compared to SP amplitude value by tone burst stimulation. ECochG would be extremely useful in the diagnosis of MD if we use the SP/AP area ratio (sensitivity: 88.5%); therefore, it changes the bad reputation of ECochG sensitivity using SP/AP amplitude ratio (sensitivity: 51.7%) for the diagnosis of MD.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".