Vestibular schwannoma: how much residual hearing is useful?
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine the usefulness of residual hearing in the tumour ear in vestibular schwannoma patients. DESIGN: A prospective case series study. SETTING: The study was performed at the Queen Elizabeth Health Sciences Centre in Halifax, Nova Scotia. METHODS: Thirty-three vestibular schwannoma patients and 13 controls underwent QuickSIN (Etymotic Research Inc, Elk Grove, IL) speech-in-noise testing with the tumour and good ears open and occluded. Nine testing conditions used three speakers with speech signal from the tumour side, the front, and the good ear side, with noise in other speakers. MAIN OUTCOME MEASURES: Tumour ear contribution (TEC) was calculated by the decrease in score with the index ear occluded. Multiple regression analysis and correlation coefficients were used to determine predictors of TEC. RESULTS: The strongest correlation was between the pure-tone average (PTA) and the TEC with signal from the tumour side. The speech discrimination score (SDS) was also significantly correlated with TEC in this condition. Neither PTA nor SDS correlated well with TEC with signal from other directions. Multiple regression analysis with TEC and sound from the tumour ear as the dependent variable showed that the SDS and PTA of the tumour ear are significant independent predictors (p = .049 and .037, respectively). There are no obvious breakpoints in the PTA or SDS to make 50%, 50 dB, or other operating points "special." CONCLUSION: The main contribution of residual hearing is in signals from the tumour side. The various rules are more or less equivalent in discriminating between those who will have a high TEC and those who will not.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".