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 and 4). Figure 1: Audiogram Legend
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".