Effect of transcutaneous neuromodulation on predictive parameters of extubation failure in severe acute pancreatitis: A case report
Bibliographic record
Abstract
Background: Complications of pancreatitis can lead to admission to the intensive care unit (ICU) with invasive mechanical ventilation. Reducing the duration of mechanical ventilation is challenging for critical care practitioners. Respiratory muscle weakness hinders the weaning process, thereby increasing the duration of mechanical ventilation and hindering pulmonary rehabilitation. Methods: We evaluated the effect of transcutaneous neuromodulation on predictors of extubation failure. The patient was a 51-year-old male with a history of type 2 diabetes, obesity (body mass index=35), and regular alcohol consumption of 40 g/day. The patient was admitted to ICU with a diagnosis of severe acute pancreatitis and multi-organ failure. Maximum inspiratory pressure (MIP), airway occlusion pressure at 100 ms (P0.1), rapid shallow breathing index (RSBI), and diaphragmatic thickening fraction (DTf) were measured. Results: The results demonstrated an improvement in all the parameters. Show an increase in MIP from -18 cmH2O to -37 cmH2O and a reduction in P0.1 from -5.7 cmH2O to -3.1 cmH2O. RSBI decreased from 107 to 72, and DTf increased from 20% to 35%. The patient was extubated successfully and discharged to the ward after a 28-day ICU stay. Conclusions: The application of transcutaneous neuromodulation led to an improvement in the predictive parameters of extubation failure in patients with severe acute pancreatitis, which was ultimately confirmed by ventilatory support not being required after extubation. Transcutaneous neuromodulation application helps improve respiratory parameters and systemic improvement of the patient until he is released from ICU. Transcutaneous neuromodulation should be used in combination with other physiotherapy techniques and should be included in a comprehensive rehabilitation protocol rather than as an isolated therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".