Establishing and integrating a transoral robotic surgery programme into routine oncological management of head and neck cancer – a UK perspective
Bibliographic record
Abstract
BACKGROUND: The introduction of transoral robotic surgery into routine management of patients is complex. It involves organisational, logistical and clinical challenges. This study presents our experience of implementing such a programme and provides a blueprint for other centres willing to establish similar services. METHODS: Implementation of the robotic surgery programme focused on several key domains: training, logistics, governance, multidisciplinary team awareness, pre-operative imaging, anaesthesia, post-operative care, finance, patient selection and consent. Programme outcomes were evaluated by assessing operative outcomes of the first 117 procedures performed. RESULTS: The success of the transoral robotic surgery programme has been possible because of the scrupulous planning phase before the first procedure, and the time invested on team awareness and training. CONCLUSION: Implementation of a new transoral robotic surgery service has led to: the development of a dedicated transoral robotic surgery patient care protocol, the performance of progressively more complex procedures, the inclusion of transoral robotic surgery training and the establishment of several research projects.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".