UTIs and chest infection are associated with higher disability in algorithmically identified multiple sclerosis
Bibliographic record
Abstract
Introduction An algorithmically identified Multiple Sclerosis (MS) population in Wales using a diagnostic method based on McDonald criteria alone had less severe disease compared to those with multiple encounters. We investigated the nature of the encounters. Method MS subjects were identified using two algorithms, ‘Manitoba10’ based on multiple healthcare encounters and based on McDonald criteria minus the Manitoba10 population ‘Not Manitoba10’. Top 10 primary admissions, number of chest infections (ICD10 J00-J06, J09-J18, J20-J22) and UTIs (ICD10 N390) were compared to the general population. Results 1,845,360/4,616,124 subjects in the general population had a UTI or chest infection, versus (4,166/5,438) Manitoba10 and (2,346/4,153) Not Manitoba10. Mean UTI and chest infection admissions were significantly higher in Manitoba10 versus the general population (UTI 1.45,p<0.001, Chest 0.79,p<0.001); in Not Manitoba10 only UTI was significant (UTI 0.45,p<0.001, Chest 0.48,p=0.464). Manitoba10 cohort had significantly higher UTI/Chest infection admissions compared to Not Manitoba10 (p<0.001). Of the top 10 primary causes of hospital admission, Manitoba10 features more events related to increasing disability (Holiday relief care, sepsis, pneumonitis), whereas the general population has more frequent admissions for cancer related events and UTIs. Discussion MS subjects with multiple encounters have more frequent hospital admissions for UTIs and chest infections potentially contributing to more severe disease.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".