‘How does that sound?’: objective and subjective voice outcomes following CO<sub>2</sub> laser resection for early glottic cancer
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of transoral laser microsurgery for early glottic cancer on subjective and objective vocal outcome measures. DESIGN: Prospective cohort study. SETTING: Tertiary care cancer centre. PARTICIPANTS: All patients scheduled for transoral laser microsurgery for untreated early primary glottic cancer over a 22-month period and offered voice assessment (31 patients; 19 tumour stage one, 12 tumour stage two). MAIN OUTCOME MEASURES: Fundamental frequency, maximum phonation time, calculated jitter, shimmer and subjective voice rating, analysed by tumour stage. RESULTS: Tumour stage T1 patients had significantly different fundamental frequencies and maximum phonation times at three months post-operatively, compared with pre-operative values; these differences resolved by 12 months. At 12 months, tumour stage T2 patients had significantly shorter maximum phonation times, and all patients reported significantly worse subjective voice ratings, compared with pre-operative values. CONCLUSION: We found no change in fundamental frequency, jitter and shimmer, one year post-operatively. Maximum phonation time deteriorated but stage one patients appeared to compensate, whereas stage two patients did not. Resection size may be a factor. All patients reported significantly worse subjective voice ratings at one year. Aerodynamic and subjective voice measures appear most sensitive to change in this patient group.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".