Wideband energy reflectance patterns in preoperative and post-operative otosclerotic ears
Bibliographic record
Abstract
The purpose of this study was to examine patterns of energy of reflectance (ER) in preoperative and post-operative conditions in otosclerotic ears. It was also within the scope of this paper to investigate whether the changes in ER pattern post-operatively correlate to the improvement in hearing as measured by pure-tone audiometry. ER was measured in 15 surgically confirmed otosclerotic ears (mean age: 44 years) before and after the stapes surgery. The most prominent change in ER pattern following the surgery in majority of the subjects was a sharp and deep drop in ER value between 700–1000 Hz. There was also a secondary wider and smaller increase in ER value following the surgery between 2000–4000 Hz. Moreover, in most cases the drop in ER values following the surgery at low frequencies (≤1000 Hz) approximated the mean ER values in the normal group. Comparison of ER patterns with hearing improvement as measured by air conduction averaged across low (250–1000 Hz) and high (2000–6000 Hz) frequency bands before and after the surgery did not reveal any significant correlation. However, there was a general positive correlation trend for ER changes in low frequency band and AC changes, especially for high frequency bands. Changes in ER pattern may potentially be useful as an objective tool for monitoring the impact of the stapes reconstructive surgery and evaluating different surgical protocol.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".