Sudden Neural Hearing Loss and an Unexpected Recovery: A Case Report
Bibliographic record
Abstract
A 26-year-old nursing student presented for an audiogram (Figure 1: Audiogram Legend).Seen nine years earlier by the senior author, she had recently become aware of an increase in her hearing acuity.She presented as a teen with a sudden left SNHL and tinnitus without dizziness.There had been no history of illness or otic trauma, and she was initially treated at the emergency room (ER) with a course of prednisone.An ENT consult was arranged.Her past medical history was non-contributory, and the ENT exam was within normal.Her initial audiogram showed normal right-sided hearing and a left moderate to severe SNHL with an SRT of 55dB and a WDS of 72 % (See Figure 2).An IAC MRI was done and was normal.A second course of oral prednisone was prescribed, but follow-up audiograms showed no improvement (See Figure 3 and4).Figure 1: Audiogram Legend
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".