Open Partial Horizontal Laryngectomy as a Conservative Salvage Treatment for Laser-Recurrent Laryngeal Cancer: A Multi-Institutional Series
Bibliographic record
Abstract
Early-stage laryngeal cancer (T1-T2) is commonly treated with organ-preserving techniques such as transoral laser microsurgery (TOLMS) or radiation therapy (RT), both providing comparable oncological outcomes but differing in functional results. Local recurrence occurs in approximately 10% of cases, making salvage surgery a crucial therapeutic option. This multi-institutional study investigates the efficacy of open partial horizontal laryngectomy (OPHL) as a salvage treatment, following recurrent laryngeal squamous-cell carcinoma (LSCC) after failed TOLMS. This analysis includes 66 patients who underwent OPHL between 1995 and 2017, reporting favorable oncological outcomes with overall survival (OS) of 87.4%, disease-specific survival (DSS) of 93.4%, and disease-free survival (DFS) of 85.5%. A recurrence rate of 10.6% was observed post-salvage OPHL, with vascular invasion and advanced pathological staging identified as significant predictors of recurrence. OPHL emerged as an effective organ-preserving alternative to total laryngectomy (TL) in select patients, especially those with limited tumor spread and preserved laryngeal function. The study highlights the importance of careful patient selection and thorough preoperative assessment to improve outcomes, positioning OPHL as a key option in treating recurrent laryngeal cancer and offering oncological control while preserving laryngeal functions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".