What “big population data” tells us about neurological disorders comorbidity
Bibliographic record
Abstract
Objective: To use a large population dataset to examine neurological disorder comorbidity. Seventeen main classes of Diagnosed International Classification of Disease (ICD) disorder codes were grouped and compared to ICD-9 Nerurological disorder codes.Methods: Calgary, Alberta, health zone diagnosis, sex and age data from 1994-2009 physician billings (n = 763,449) were grouped and tallied on the basis of the presence or absence of any neurological disorder across the 17 remaining ICD main disorder classes and represented as odds ratios (ORs with 95% confidence intervals).Results: Within the ICD categories the 17 classes were ranked by ORs: Ill-defined conditions (OR 7.42), musculoskeletal and connective tissue system disorders (OR 4.22), and psychiatric disorders (OR 3.81) were the ranked the highest main classes, respectively. Thirteen additonal main classes had ORs greaeter than 2.00.Conclusions: There was a strong relationship between neurological disorders and the ICD main classes. The results of this broad stroke analysis point to the requirement for analysis of the both the temporal relationships (e.g., before vs. after) between neurological disorders and comorbid disorderss as well as more fine-grained description of the specifice intra-class disorders underlying the reported odds ratios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".