Quality of Life following Ear Surgery Measured by the 36-Item Short Form Health Survey and the Glasgow Benefit Inventory
Bibliographic record
Abstract
BACKGROUND: Quality of life (QOL) following ear surgery is an important outcome measure. Most QOL studies are retrospective and therefore biased by uncertainties about preoperative QOL. OBJECTIVES: The primary objective was to prospectively assess change in QOL following ear surgery. The secondary objective was to determine if QOL was associated with audiometric change. METHODS: Twenty-six patients undergoing ear surgery were studied prospectively between 1999 and 2000. Before and after surgery, patients received a detailed audiometric evaluation and completed a generic (36-Item Short Form Health Survey [SF-36]) and a disease-specific (Glasgow Benefit Inventory [GBI]) QOL instrument. RESULTS: Significant audiometric improvement was noted following surgery. For six of the eight subsections of the SF-36, there was significant improvement following surgery. Three subsections correlated with the reduction in air-bone gap. GBI scores indicated that 50% of patients experienced an increase in QOL. The GBI correlated with one subsection of the SF-36. Higher GBI scores were associated with a reduction in the air-bone gap, but there was no significant correlation. CONCLUSIONS: QOL improved following ear surgery. QOL can be effectively assessed using both generic and disease-specific instruments. Improvement in the SF-36 score was correlated with reduction of the air-bone gap, suggesting that improved hearing was a determinant of improved QOL.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".