Bibliographic record
Abstract
Th e fi edition of Morbid Obesity: Peri-operative Manage ment, published in 2005, was heralded as an important and timely comprehensive review of perioperative care of morbidly obese surgical patients.Adrian Alvarez has built on the success of the fi rst book and, with the help of three new editors, has presented a concise and well-written text about the complexities of dealing with morbidly obese surgical patients.In this second edition, the 25 chapters are arranged in fi ve general sections.Th e text is well illustrated and competently organized.Th e authors come from varied clinical backgrounds and include anesthesiologists, surgeons, and intensivists.Each chapter is well researched and appropriately referenced and deals with all aspects of care of bariatric surgical patients.Th e authors start with a discussion of specifi c challenges of the pathophysiology in the bariatric population and then move on to preparation and pre-operative management, intra-operative management, and postoperative care of bariatric surgery patients.Th e text has minimal repetition and fl ows very well from one section to the next.New features in this edition include chapters on the pathophysiology of pneumoperitoneum, postoperative rhabdomyolysis, informed consent, and bariatric surgery in adolescents.Th e chapters on positioning, monitoring, airway manage ment, drug dosing, ventilatory strategies, co morbidities, and post-operative care all include information that would be extremely relevant to the practice of both anesthetists and intensivists.Some chapters (for example, the ones dealing with informed consent, renal
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".