Anaesthetic complications in plastic surgery
Bibliographic record
Abstract
Anaesthesia related complications in plastic surgeries are fortunately rare, but potentially catastrophic. Maintaining patient safety in the operating room is a major concern of anaesthesiologists, surgeons, hospitals and surgical facilities. Circumventing preventable complications is essential and pressure to avoid these complications in cosmetic surgery is increasing. Key aspects of patient safety in the operating room are outlined, including patient positioning, airway management and issues related to some specific conditions, essential for minimizing post-operative morbidity. Risks associated with extremes of age in the plastic surgery population, may be minimised by a better understanding of the physiologic changes as well as the pre-operative and post-operative considerations in caring for this special group of patients. An understanding of the anaesthesiologist's concerns during paediatric plastic surgical procedures can facilitate the coordination of efforts between the multiple services involved in the care of these children. Finally, the reader will have a better understanding of the perioperative care of unique populations including the morbidly obese and the elderly. Attention to detail in these aspects of patient safety can help avoid unnecessary complication and significantly improve the patients' experience and surgical outcome.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".