The Comorbidity of Neurological Disorders in a Population
Bibliographic record
Abstract
Aim The comorbidity of neurological disorders and physical (biomedical/somatic) disorders was examined. Diagnosed ICD disorders (independent variable) were grouped into 17 categories based on ICD 9 codes 001-319 and 360-999. Neurological disorders (dependent variable) were classified as ICD-9 codes 320-359. Materials and Methods We used direct physician billing data for the city of Calgary, Alberta from 1994-2009 for treatment of any presenting concern in the Calgary health zone (n = 763449). The counts of individuals with and without other disorders were tallied and grouped on the basis of the presence or absence of any neurological disorder. Odds ratios (OR) and 95% confidence intervals of the association were calculated and compared. Results Diagnosed disorders were ranked by OR with the ICD categories: Symptoms, signs, and ill-defined conditions (OR 7.42), musculoskeletal system and connective tissue (OR 4.22), and mental disorders (OR 3.81) were the highest respectively. Thirteen categories had OR above 2.00. Conclusions Neurological disorders have a strong relationship with a range of disorders, which indicates the need for a more detailed analysis of the temporal relationship between these disorders, in order to illuminate the etiology and sequelae of neurological and other disorders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".