Which Is Mightier, the Tuning Fork or the Bone Oscillator?
Bibliographic record
Abstract
INTRODUCTION: It is important to differentiate cochlear implant candidates with profound sensorineural hearing loss from those with profound mixed hearing loss. The latter include patients with far advanced otosclerosis and chronic otitis media who may be better treated with ossiculoplasty and conventional amplification than with cochlear implantation. Otologists have observed that a dentally placed tuning fork can be heard by some patients whose sensorineural thresholds are beyond the limits of a bone oscillator placed on the mastoid. We hypothesized that tuning forks may be able to deliver a strongerintensity bone-conducted signal than a conventional mastoid-placed oscillator. OBJECTIVE: To measure the maximum bone-conduction signal intensities of a mastoid-placed bone oscillator and tuning forks placed on the forehead, mastoid, and teeth. METHOD: The maximum signal intensity of a mastoid-placed bone oscillator and tuning forks at various locations (mastoid, forehead, teeth) was measured using behavioural masking level differences at three frequencies (250, 500, and 1000 Hz). RESULTS: The peak intensity of a dental bone-conducted tuning fork signal is greater than that delivered by a mastoid-placed bone oscillator (at least 20.5 dB HL at 250 Hz, 16.5 dB HL at 500 Hz, and 8.5 dB HL at 1000 Hz; p <.001) at all three frequencies tested. At some frequencies, the bone oscillator's maximum perceived level is greater than the peak perceived level of the tuning fork when placed on the forehead or mastoid. CONCLUSIONS: In addition to pure-tone audiometry, all patients being considered for cochlear implantation should be evaluated with maximally vibrating tuning forks applied to the teeth. If the signal is audible, other surgical procedures may need to be considered before proceeding with cochlear implantation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".