Ultrasonographic Evaluation of Diaphragm Thickness During Mechanical Ventilation in Intensive Care Patients
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Mechanical ventilation is associated with atrophy and weakness of the diaphragm. Ultrasound is an easy noninvasive way to track changes in thickness of the diaphragm. OBJECTIVE: To validate ultrasound as a means of tracking thickness of the diaphragm in patients undergoing mechanical ventilation by evaluating interobserver and interoperator reliability and to collect initial data on the relationship of mode of ventilation to changes in the diaphragm. METHODS: Daily ultrasound images of the quadriceps and the right side of the diaphragm were acquired in 8 critically ill patients receiving various modes of mechanical ventilation. Thickness of the diaphragm and the quadriceps was measured, and changes with time were noted. Interoperator and interobserver reliability were measured. RESULTS: Intraclass correlation coefficients between operators and between observers for thickness of the diaphragm and quadriceps were greater than 0.95, indicating excellent interoperator and interobserver reliability. Patients receiving assist-control ventilation (n = 4) showed a mean decline in diaphragm thickness of 4.7% per day. Patients receiving pressure support ventilation (n = 8) showed a mean increase in diaphragm thickness of 1.5% per day. Quadriceps thickness declined in all participants (n = 8) at a mean rate of 2.0% per day. CONCLUSIONS: Use of ultrasound to measure thickness of the diaphragm in 8 intensive care patients undergoing various modes of mechanical ventilation was feasible and yielded reproducible results. Ultrasound tracking of changes in thickness of the diaphragm in this small sample indicated that the thickness decreased during assist-control mode and increased during pressure support mode.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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