Bibliographic record
Abstract
PURPOSE OF REVIEW: Pneumonectomy has the highest perioperative risk among common pulmonary resections. The purpose of this review is to update clinicians on the importance of anesthetic management for these patients. RECENT FINDINGS: Two complications associated with increased perioperative mortality are relevant to anesthetic management: postoperative arrhythmias and acute lung injury. The geriatric population is particularly at risk for arrhythmias. Adequate preoperative cardiac assessment and drug prophylaxis may decrease this risk. Patients with decreased respiratory function are at increased risk for acute lung injury. The use of large tidal-volume ventilation during anesthesia may increase this risk. There is a trend to better outcomes in centers with larger surgical volumes. SUMMARY: Patients should have a preoperative assessment of their respiratory function in three areas: lung mechanical function, pulmonary parenchymal function and cardiopulmonary reserve. Interventions that have been shown to decrease the incidence of respiratory complications include cessation of smoking, physiotherapy and thoracic epidural analgesia. Extrapleural pneumonectomy and sleeve pneumonectomy are surgical variations that place specific increased demands on the anesthesiologist. The rare but treatable complication of cardiac herniation must always be remembered as a potential cause of life-threatening hemodynamic instability in the early postoperative period.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".