Does Anticholinergics Drugs Burden Relates To Global Neuro-disability Outcome Measures And Length Of Hospital Stay
Bibliographic record
Abstract
following concussion. Data was analyzed using a mixed-effects modeling approach. Setting: Baseline and concussion follow-up testing took place in the BrainFit lab at the University of Toronto. Participants: A convenience sample of 211 youth hockey players between 8 to 15 years of age from hockey teams in the GTA was recruited across a four-year period. Interventions: Not applicable. Main Outcome Measure(s): The Developmental Neuropsychological Assessment (NEPSY) was used to assess VF in both semantic and phonemic domains. The Rey Auditory Verbal Learning Test (RAVLT) was used to assess VLM. Results: Baseline analyses revealed significant age and gender effects on measures of VF and VLM. Multiple effects of concussion history on measures of VF and VLM were also found. Conclusions: Age, gender and concussion history have effects on VF and VLM in youth athletes and these factors must be considered in the clinical management of concussion. Findings have functional implications for returning to daily activity as undetected neurocognitive impairments put youth hockey players at increased risk for re-injury and further possible deleterious outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".