Diaphragm pacing in spinal cord injury can significantly decrease mechanical ventilation in multicenter prospective evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it