Enteral nutrition in amyotrophic lateral sclerosis (ALS): Canadian practices
Bibliographic record
Abstract
Background: Dysphagia from ALS may be treated by enteral nutrition; however criteria for timing of feeding tube placement has not been well studied. The aim of this project was to better understand the practice of enteral nutrition management within Canadian ALS clinics. Methods: ALS clinics were asked if they had written guidelines for timing of PEG insertion and if not, what criteria they use to make this decision. Results: Responses from 10 of 17 clinics were received. One clinic had written guidelines. Most used decline in respiratory function, dysphagia, weight loss or some combination of all three. Six clinics reported dropping FVC, ranging from 70% to 50% as prompting tube insertion. Five clinics reported weight loss as part of their criteria. Dysphagia was reported as the most important factor by 7 clinics. Psychological readiness for tube placement was a key factor in 3 clinics. Some clinics comment they place tubes in advance of dysphagia. Conclusion: Criteria for tube insertion varies between clinics. Practices generally reflect published recommendations, but vary on the emphasis of specific criteria. The lack of strong scientific evidence to guide decisions may contribute to management variability. Further study is needed to guide practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".