Bibliographic record
Abstract
Before 1964, electrocochleography (ECochG) was a surgical procedure carried out in the operating theatre. Currently, the newest application is also an intra-operative one, often carried out in conjunction with cochlear implant surgery. Starting in 1967, the recording methods became either minimal- or not-invasive, i.e., trans-tympanic (TT) or extra tympanic (ET), and included extensive studies of the arguments pro and con. I will review several valuable applications of ECochG, from a historical point of view, but covering all 75 years if applicable. The main topics will be: (1) comparing human and animal cochlear electrophysiology; (2) the use in objective audiometry involving tone pip stimulation-currently mostly pre cochlear implantation but otherwise replaced by auditory brainstem response (ABR) recordings; (3) attempts to diagnose Ménière's disease and the role of the summating potential (SP); (4) early use in diagnosing vestibular schwannomas-now taken over by ABR screening and MRI confirmation; (5) relating human electrophysiology to the effects of genes as in auditory neuropathy; and (6) intracochlear recording using the cochlear implant electrodes. The last two applications are the most recently added ones. The "historical aspects" of this review article will highlight the founding years prior to 1980 when relevant. A survey of articles on Pubmed shows several ups and downs in the clinical interest as reflected in the publication counts over the last 75 years.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".