Treatment of early-stage glottic cancer: meta-analysis comparison of laser excision versus radiotherapy.
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this study was to conduct a meta-analysis to compare the oncologic outcomes of external radiation (XRT) and transoral laser (TOL) surgical excision in the treatment of early-stage glottic cancer. The secondary outcome examined was posttreatment voice quality. DESIGN: Meta-analysis. METHOD: Systematic methods were used to identify published and unpublished data. Two reviewers screened all titles and abstracts for relevance and independently assessed all articles. All identified studies were retrospective. MAIN OUTCOME MEASURES: Local control, overall survival, laryngectomy-free survival, and posttreatment voice quality. RESULTS: For oncologic control, case series were pooled as a composite group using a random effects model. The analysis was based on over 7600 patients. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. There were no significant differences between TOL surgery and XRT for local control (OR 0.81, 95% CI 0.51-1.3) and laryngectomy-free survival (OR 0.73, 95% CI 0.39-1.35). For overall survival, the analysis favoured TOL surgery (OR 1.48, 95% CI 1.19-1.85). For voice quality, there were no objective differences; however, there was a trend toward superiority for XRT. CONCLUSIONS: This is the first study to examine the management of early glottic cancer using meta-analytic methodology. The analysis shows that although there is a trend favouring TOL surgery for overall survival, there is no clear difference in oncologic outcome between TOL surgery and XRT. However, there is a trend toward improved posttreatment voice quality with XRT. This is of questionable clinical significance as objective voice analyses often do not correlate with subjective assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.055 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".