Chapter 8 Upper gastrointestinal and mediastinal surgery
Bibliographic record
Abstract
Extract Thymectomy ... Introduction Tumours of the thymus are rare, with an incidence of 1–5 per million population, per year. Thymomas are the commonest mediastinal tumour in adults, followed by lymphomas. ... Treatment options Surgical ... Pre-operative assessment and preparation (See Pre-op assessment) History and optimization Dose of anticholinesterase Relevent investigations Pre-operative planning to avoid post-operative ventilation ... Anaesthesia for thymectomy Induction ... Maintenance Neuromuscular blocking drugs and MG ... Post-operative Special considerations ... Further reading Leventhal SR, Orkin FK, Hirsh RA. Prediction of the need for postoperative mechanical ventilation in myasthenia gravis. Anesthesiology 1980; 53(1):26–30.10.1097/00000542-198007000-00006CrossrefPubMedWeb of ScienceClose Naguib M, el Dawlatly AA, Ashour M, et al. Multivariate determinants of the need for postoperative ventilation in myasthenia gravis. Canadian Journal of Anaesthesia 1996; 43(10):1006–13.10.1007/BF03011901CrossrefPubMedWeb of ScienceClose Eisenkraft JB, Book WJ, Mann SM, et al. Resistance to Succinylcholine in Myasthenia Gravis: A Dose-response Study. Anesthesiology 1988; 69(5):760–2.10.1097/00000542-198811000-00021CrossrefPubMedWeb of ScienceClose
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.182 | 0.090 |
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".