Management of positive resection margins following transoral laser microsurgery for glottic cancer
Bibliographic record
Abstract
Abstract Objectives The current literature provides limited guidance on the management of positive margins (PMs) following transoral laser microsurgery (TLM) for glottic squamous cell carcinoma (SCC). Long‐term data exploring the treatment of PMs with both initial observation and re‐resection are limited. Our objective was to determine the optimal treatment for PM patients following TLM for glottic SCC. Methods Clinical information on glottic SCC patients with PMs following treatment with TLM was prospectively collected at our institution from 2007 to 2018. We use a laryngeal template during the initial TLM where the area of resection is outlined for future reference. Data were compared with univariate analysis and survival plots were generated using the Kaplan–Meier method. Results A total of 29 patients with PMs were treated with either re‐resection (19 patients), close observation (6 patients), or adjuvant radiation alone (4 patients). Re‐resection patients had SCC or severe dysplasia on initial margin pathology and 23% with early‐stage disease had recurrence (T1–T2). Five (83%) patients who underwent close observation required re‐resection based on clinical suspicion of recurrence (confirmed on final pathology), which was significantly different from the re‐resection patients ( p < .05). Close observation was therefore discontinued as a management of PMs. Four patients (21%) had no residual malignancy on re‐resection specimens. Deep margins only accounted for 17% of all PMs. Disease‐specific survival for all PM patients at 5 years was 82.4% (SE 9.6%, CI 53.4%–91.6%). Conclusions Our long‐term experience with treating early‐stage glottic SCC with TLM supports re‐resection as an appropriate management for cases of PMs. Level of Evidence 4.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".