Transoral Robotic Surgery and the Unknown Primary: A Cost‐Effectiveness Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the cost-effectiveness of transoral robotic surgery (TORS) for the diagnosis and treatment of cervical unknown primary squamous cell carcinoma (CUP). STUDY DESIGN: Case series with chart review. SETTING: Tertiary academic hospital. SUBJECTS AND METHODS: A retrospective chart review was performed on patients with new occult primary squamous cell carcinoma of the head and neck with nondiagnostic imaging and/or endoscopy who were treated with TORS at a tertiary hospital between 2009 and 2012. Direct costs were obtained from the hospital's billing system, and national data were used for inpatient hospital costs and physician fees. The proportion of tumors found in 3 strategies was used as effectiveness to calculate the incremental cost-effectiveness ratio. RESULTS: In total, 206 head and neck robotic cases were performed at our institution between December 2009 and December 2012. Three surgeons performed TORS on 22 patients for occult primary squamous cell carcinoma. The primary tumor was located in 19 of 22 patients (86.4%). The incremental cost-effectiveness ratio for sequential and simultaneous examination under anesthesia with tonsillectomy (EUA) and TORS base of tongue resection was $8619 and $5774 per additional primary identified, respectively. CONCLUSION: Sequential EUA followed by TORS is associated with an incremental cost-effectiveness ratio of $8619 compared with traditional EUA alone. Bilateral base of tongue resection should be considered in the workup of these patients, particularly if the palatine tonsils have already been removed.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".