Oncologic and voice outcomes after treatment of early glottic cancer: transoral laser microsurgery versus radiotherapy.
Bibliographic record
Abstract
OBJECTIVE: To compare the laryngeal preservation rates and voice outcomes after treatment of early glottic cancer between transoral laser microsurgery (TLM) and radiotherapy (RT). DESIGN: Multicenter, retrospective consecutive cohort of stage 1 and 2 glottic carcinoma treated with TLM or RT. SETTING: Three Canadian academic cancer centres. METHODS AND MAIN OUTCOME MEASURES: The patients were those of the regional cancer registries associated with each of the participating universities between 2002 and 2010. The primary oncologic end point was organ preservation. The primary functional outcome measure was the Voice Handicap Index (VHI-10). RESULTS: A total of 234 patients were treated for early glottic cancer (143 TLM, 91 RT). At 2 years, the laryngeal preservation rate for stage 1 disease was 100% TLM and 92% RT (p < .004); for stage 2 disease, it was 100% TLM and 88% RT (p = not significant). There was only one laryngectomy in the TLM group over 5 years posttreatment. There were functional data on 132 patients (83 TLM, 49 RT). Median VHI-10 scores were inferior for laser patients at all three time intervals (6, 12, and 24-48 months posttreament) despite a stage bias in favour of TLM (range of median VHI score over time intervals: TLM = 9.5-12, RT = 3.5-8; p = .01-.08). However, theses scores represent mild disability in both groups. CONCLUSIONS: TLM patients have poorer voice quality than RT patients. However, the advantages of TLM in most patients outweigh the degree of voice handicap. Organ preservation rates for TLM were better than or equal to those of RT for both stage 1 and 2 glottic cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".