Respiratory prehabilitation in pediatric anesthesia in children with muscular and neurologic disease
Bibliographic record
Abstract
Children with neuromuscular, chronic neurologic, and chest wall diseases are at increased risk of postoperative respiratory complications including atelectasis, pneumonia, and respiratory failure with the possible need for reintubation or even tracheostomy. These complications negatively impact patient outcomes, including increased healthcare resource utilization and increased surgical mortality. In these children, the existing respiratory reserve is often inadequate to withstand the stresses brought on during anesthesia and surgery. A thorough clinical assessment and objective evaluation of pulmonary function and gas exchange can help identify which children are at particular risk for poor postoperative outcomes and thus merit preoperative interventions. These may include initiation and optimization of non-invasive ventilation and mechanical insufflation-exsufflation. Furthermore, such an evaluation will help identify children who may require a postoperative extubation plan tailored to neuromuscular diseases. Such strategies may include avoidance of pre-extubation lung decruitment by precluding continuous positive airway pressure trials, aggressively weaning to room air and directly extubating to non-invasive ventilation with a high inspiratory to expiratory pressure differential of at least 10 cm H20. Children with cerebral palsy and other neurodegenerative or neurodevelopmental disorders are a more heterogeneous group of children who may share some operative risk factors with children with neuromuscular disease; they may also be at risk of sleep-disordered breathing, may also require non-invasive ventilation or mechanical insufflation-exsufflation, and may have associated chronic lung disease from aspirations that may require perioperative treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".