Functional and Oncologic Outcomes of Octogenarians Undergoing Transoral Laser Microsurgery for Laryngeal Cancer
Bibliographic record
Abstract
OBJECTIVE: To evaluate the oncologic and functional outcomes of transoral laser microsurgery (TLM) for glottic cancers in patients ≥80 years. STUDY DESIGN: Prospectively collected case series. SETTING: QEII Health Sciences Centre, Halifax, Canada. METHODS: This case series used a prospectively collected glottic cancer database to examine consecutive elderly patients (≥80 years old) undergoing TLM. Kaplan-Meier analysis was used to evaluate rates of disease-free, disease-specific, and overall survival as the primary end points of oncologic control. Secondary functional outcomes included voice function, length of hospital stay, and time to readmission. RESULTS: From 2005 to 2017, 17 octogenarian patients underwent TLM for glottic cancer. Median follow-up was 4.19 years (interquartile range, 0.71-6.95). Kaplan-Meier estimates of 5-year survival were 78.4% (disease free), 92.9% (disease specific), and 81.9% (overall). The median length of hospital stay was 1 day (range, 0-8). There was only 1 readmission within 30 days of surgery. No patients in this study developed significant surgical or postoperative complications requiring unplanned readmissions. Patient-perceived voice function improved to normal after treatment in 62.5% of patients. CONCLUSION: The results of this study suggest that TLM is a safe and effective treatment modality for glottic cancer in patients aged ≥80 years, providing good oncologic control and satisfactory functional outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".