Clinicopathological Characteristics, Treatment Patterns, and Outcomes in Patients with Laryngeal Cancer
Bibliographic record
Abstract
BACKGROUND: Various factors can affect the survival of patients with laryngeal cancer (LC). In this retrospective study, we assessed clinicopathological features, their prognostic value, and treatment modalities for patients with confirmed squamous cell LC. METHODS: We collected patient data on demographics, clinicopathological characteristics, treatment patterns, and outcomes. The primary endpoints were overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), and locoregional control (LRC). We assessed survival using the Kaplan-Meier method and Cox regression model analyses of potential prognostic parameters. RESULTS: After a median follow-up of 76 months, 28 (33.3%) patients had a recurrence. The median OS was 78 months, with an event recorded in 50% of patients. The DSS median was not reached (NR) with a survival rate of 72.6%, the DFS survival rate was 66.7% with median NR, and the LRC survival rate was 72.6% with median NR. After conducting a multivariate analysis of significant variables, we found that only recurrence and lymphatic invasion had an independent effect on OS and recurrence in DSS, while subsite impacted DFS and LRC. CONCLUSIONS: Survival trends were consistent with other studies, except for OS. Recurrence, lymphatic invasion, and subsite location were significant factors that impacted patient survival.
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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 | 0.000 | 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".