Is there a case for diaphragm pacing for amyotrophic lateral sclerosis patients?
Bibliographic record
Abstract
Respiratory pacing has advanced the long-term management of respiratory failure secondary to neurological disorders. It has an established role in curtailing invasive mechanical ventilation after upper motor neuron lesions such as spinal cord injury. There is increasing interest to expand the application of intramuscular diaphragm pacing to amyotrophic lateral sclerosis (ALS), a progressive and fatal neurodegenerative disease. Although diaphragm pacing has been offered to ALS patients, evidence-based data to determine its benefits remain lacking. The limited current literature indicates progression of respiratory dysfunction in ALS patients despite diaphragm pacing. The data from clinical trials are inadequate to substantiate its survival and sleep benefits. Its advantages over non-invasive mechanical ventilation have not been directly investigated. Furthermore, there are cautions for ALS patients to consider when opting for diaphragm pacing. Progressive degeneration of the phrenic motor neurons in classic ALS will interrupt the transmission of pacer signals to sustain diaphragm contractions. Pacing protocols that are safe for other neurological conditions may be detrimental for ALS, at least as suggested by transgenic animal models. Issues inherent to the device warrant expert intervention in implanted patients. At present, clinical effectiveness and long-term safety concerns about diaphragm pacing in ALS remain to be addressed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".