Perioperative Mortality in Oncologic Head and Neck Surgery
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to establish the causes of perioperative mortality after head and neck oncologic surgery, to improve operative strategies and surgery procedures, and to reduce postoperative complications. SETTING: University Hospitals of Strasbourg, Head and Neck Department. PATIENTS AND METHODS: The medical files of patients who died within 30 days of presentation with epidermoid carcinoma of the head or neck were analyzed; criteria included age, sex, medical history, and the location and stage of development of the tumour. MEASUREMENT METHOD: The causes of death are discussed with reference to the pre- and postmortem observations. RESULTS: In this study, the perioperative mortality rate was 3.07%. It depended more on tumour stage and the medical history of the patient than on tumour location and the age of the patient. The responsibility of the medical team itself was involved in some cases. DISCUSSION AND CONCLUSION: The study shows the difficulty of establishing the cause of death of weakened patients who have undergone a heavy surgical operation. Although the majority of deaths recorded are considered to have been unavoidable, for a small number of them, the absence of vital function monitoring over the first days after the operation was a contributory cause. Perioperative mortality has greatly decreased over the last 30 years and is, at present, almost nonexistent during anesthesia in head and neck surgery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".