C.5 Highlighting a novel, stepwise pathway for the in-hospital management of children with acutely worsening dystonia
Bibliographic record
Abstract
Background: Dystonia is common in children with acquired and inherited neurological disorders. Status dystonicus (SD) is the most severe form of dystonia that can lead to life-threatening complications if not treated promptly. We identified a local provider knowledge gap in the acute management of dystonia, leading to uncertainty and delays in care. To our knowledge, no in-hospital clinical pathway exists for the ward-based management of acute dystonia. We hypothesized that a stepwise clinical pathway would standardize and improve comfort in managing hyperacute dystonia. Methods: We formed a multidisciplinary working group and developed a pathway based on literature review and expert consensus. Aims of the pathway included: reducing delays in recognition and treatment of acute dystonia, limiting variation in management, and decreasing progression to SD. A survey was administered to providers assessing knowledge and comfort post-implementation. Results: There has been high usability with 58% (18/31) of providers surveyed having used the pathway at least once. Provider comfort has improved, with 89% (25/28) of respondents reporting increased comfort managing SD due to the clarity of the pathway and stepwise directions. Conclusions: The pathway fills a gap in the in-hospital management of dystonia and has led to increased provider comfort.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".