Adverse Events Associated with Postsurgery of Early‐Stage Oral Cancer in a Phase III Surgical Trial
Bibliographic record
Abstract
Objectives: 1) Investigate the adverse events (AEs) associated with surgery for early‐stage oral cancer. 2) Characterize surgical procedure and its related AEs. Methods: Based on the National Institute of Health/National Cancer Institute Common Terminology Criteria for Adverse Events (v4.0), patient self‐reported AEs post‐surgery in a pan‐Canadian multi‐center phase III randomized controlled surgical trial (the COOLS trial) were captured during the follow‐up visits. Results: From September 2010 to January 2013, 228 eligible patients were recruited and received surgeries, including tumor excision, and/or free flap or neck dissection. Among these, 188 (82%) patients had at least 3 months follow‐up with an average of 24.8±19 months. There were 83 types and 593 counts of AEs from 148 (79%) patients. The most occurring AEs were oral pain (146 patients) and oral dysesthesia (64 patients). Thirty patients (20%) presented with prolonged oral pain (≥3 months) with an average duration of 6.5±10.5 months for Grade I and 2.0±5.6 months for Grade 2 or greater (77 patients). The average duration of oral dysethesia was 18.6±16.4 months with 52 (80%) over 3 months. Univariate analysis had shown that neck dissection and longer surgery time have significant impact on the reported Grade 2 oral pain (P = 0.03 for both). Interestingly, excision type [cold knife (9%) vs. electric knife/laser (23%)] had significant effect on experiencing prolonged post‐surgical oral pain (P = 0.01). Conclusions: Post‐surgical AEs surveillance is critical to understand the factors impacting patients’ quality of life. Concurrent neck dissection has impacts on patients’ experience in prolonged post‐surgical oral pain.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".