Predictors of Postoperative Radiation Following Laser Resection in Early‐Stage Glottic Cancer
Bibliographic record
Abstract
OBJECTIVE: Guideline recommendations for the treatment of early-stage glottic cancer are limited to single-modality therapy with surgery or radiation alone. We sought to investigate the clinicopathologic and treatment factors associated with the use of postoperative radiation therapy (PORT) following laser excision for patients with T1-T2N0 glottic squamous cell carcinoma (SCC). STUDY DESIGN: Retrospective observational study of the National Cancer Database. SETTING: National Cancer Database review from 2004 to 2014. PATIENTS AND METHODS: A total of 1338 patients with primary cT1-T2N0M0 glottic SCC undergoing primary laser excision were included. Hospitals were divided into quartiles based on yearly volume of laryngeal laser cases performed. Multivariate logistic regression was performed to identify independent predictors of PORT. RESULTS: The overall rate of PORT was 30.0%. Predictors of PORT included treatment at lower-volume hospitals (adjusted odds ratio [aOR] for quartiles 2-4, 1.32-4.84), positive margins (aOR, 3.83 [95% CI, 2.54-5.78]), and T2 tumors (aOR, 3.58 [95% CI, 2.24-5.74]). PORT utilization demonstrated a strong inverse correlation with hospital volume. Among top-quartile hospitals, the rate of PORT was 11.2%, while rates of PORT at second-, third-, and fourth-quartile institutions were 19.2%, 32.2%, and 37.4%, respectively. CONCLUSIONS: Predictors of PORT in multivariable analysis included treatment at lower-volume facilities, positive margins, and T2 disease. This study highlights the importance of treating early-stage glottic carcinoma at high-volume institutions. In addition, there is a need to reevaluate the use of PORT and reduce the rate of dual-modality therapy for patients with early-stage glottic SCC.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".