Diaphragm pacing in spinal cord injury can significantly decrease mechanical ventilation in multicenter prospective evaluation
Bibliographic record
Abstract
BACKGROUND: Cervical spinal cord injury (SCI) can lead to dependence on mechanical ventilation (MV) with significant morbidity and mortality. The diaphragm pacing system (DPS) was developed as an alternative to MV. METHODS: We conducted a prospective single-arm study of DPS in MV-dependent patients with high SCI and intact phrenic nerves. Following device acclimation, pacing effectiveness to provide ventilation was evaluated. The primary endpoint was the number who could use DPS to breathe for 4 continuous hours without MV. Secondary endpoints included the number of patients that could use DPS 24 h/day free of MV and the ability of DPS to maintain clinically acceptable tidal volume (Vt). In addition, we conducted a meta-analysis that included the prospective study along with data from four recently published studies to evaluate DPS hourly use. RESULTS: Fifty-three patients were implanted in the prospective study. Most were male (77.4%) with a median time from injury to treatment of 28.3 (IQR: 12.1, 83.3) months. Four- and 24-h use occurred in 96.2% (95% CI: 87.0%, 99.5%) and 58.5% (95% CI: 44.1%, 74.9%), respectively. Four and 24-h results in the meta-analysis cohort (n = 196) exhibited similar results 92.2% (95% CI: 82.6%, 96.7%) and 52.7% (95% CI: 36.2%, 68.6%) using DPS for 4 and 24 h, respectively. DPS use significantly exceeded the calculated basal tidal volume requirements by a mean of 48.4% (95% CI: 37.0, 59.9%; p < 0.001). CONCLUSIONS: This study demonstrates that in most ventilator-dependent patients, diaphragm pacing can effectively supplement or completely replace the need for MV and support basal metabolic requirements.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".