P.078 Diaphragm ultrasound in amyotrophic lateral sclerosis: a case report demonstrating a critical role for this technique
Bibliographic record
Abstract
Background: Diaphragm pacing (DP) is an experimental ALS treatment, available through a compassionate use program. Eligibility requires forced vital capacity (FVC) between 45-50% predicted and phrenic nerve conduction study (NCS) evidence showing the diaphragm can be electrically stimulated. Diaphragm ultrasound (DU) also evaluates diaphragm function by demonstrating thickening with inspiration. Methods: A 63 year old man with advanced ALS requested DP as his respiratory functions worsened. He was wheelchair bound and had severe dysarthria and dysphagia. He had exertional dyspnea and used CPAP at night for obstructive apnea. Results: FVC was 47% predicted. Initial phrenic NCS showed a normal response on the right but no response on the left, making him ineligible for DP. Diaphragm function was further assessed with DU. This showed normal thickening with inspiration bilaterally. The DU result prompted repeating the right phrenic NCS which then showed a normal response. He successfully completed surgical implantation of diaphragm leads for DP. At surgery both diaphragms showed good responses to electrical stimulation. Conclusions: Phrenic NCS can be technically challenging and yield a false positive (absent) result. In this patient, DU indicated good diaphragm function, which prompted repeating phrenic NCS. The normal phrenic NCS allowed the patient to pursue DP.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".