Poster Session 2�11:00 a.m.-7:30 p.m.�Professionals in Epilepsy care
Bibliographic record
Abstract
Marlene A. Blackman*, Elaine Wirrell† and N. Thornton**Neurosciences, Alberta Children's Hospital, Calgary, AB, Canada and †Neurosciences, Mayo Clinic, Rochester, MN Rationale: To examine the impact of epilepsy on quality of life of children compared to nearest aged siblings, as well as to further investigate the impact of epilepsy thru the use of the HARCES and ICND scales. Methods: All children with epilepsy ages 3–17 whom had a non-epileptic sibling in same age group seen thru the Neurology Clinic at the Alberta Children's Hospital were identified as potential participants for the study. Parents were asked to complete Global Quality of Life Linear scales for both children and Hague Restrictions in Epilepsy Scales and Impact of Child Neurologic Disability Scales for the child with epilepsy. Results: Fifty children with epilepsy (age range 3–17, gender M 25, F 25) and Fifty siblings who acted as the controls (age range 3–17 gender M 20 – F 30) participated in the study. Quality of life measurement using the Global Quality of Life Linear Scale was significantly different (p < 0.0001) betwen children with epilepsy (4.57 ± 0.92) and their siblings (5.30 ± 0.71). The HARCES found the average score of 18.89 Of the 49 children who completed this scale 3 (6%)scored 10 (no disability) 23 (47%) scored 11–15 (slight disability) 13 (26%) scored between 16–25,Mild 4 (8%) 26–30 moderate 6 (12%) 31–40 severe disability. On the ICNDS average was 40 with a range of (0–108) 14 scored than 20 (less impact), (28%) 20 scored 21–50 (40%) (moderate impact),with 16 scoring 50–108 (32%) (severe impact). Conclusions: Epilepsy has a negative impact on the quality of life of children, children in the same family with the same parents have statistically significant differnces in quality of life scores.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.440 | 0.137 |
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".