Differences in Bladder Care for Veterans with Multiple Sclerosis by Treatment Location
Bibliographic record
Abstract
Previous work suggested that veterans with multiple sclerosis (MS) treated through the Veterans Health Administration (VHA) had greater access to multidisciplinary care than veterans treated elsewhere. It is unknown whether other VHA-related differences in processes of MS care exist. Using the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry, we aimed to compare bladder management reported by veterans who receive care through the VHA (VHA veterans) and those who do not (non-VHA veterans). In 2005, NARCOMS participants completed the Urogenital Distress Inventory–6 (UDI-6) and reported urologic investigations and treatments. Using multivariable logistic regression, we assessed the independent association of VHA care with investigations or treatments. A total of 1247 veterans with MS responded to the questionnaire, of whom 1230 (98.6%) reported where they received their health care. Of these, 583 (47.4%) received some or all of their care through the VHA. After adjustment for demographic and clinical factors, VHA veterans more frequently reported undergoing urologic investigations than non-VHA veterans (adjusted odds ratio, 2.02; 95% confidence interval, 1.55–2.61). VHA veterans more frequently reported using pharmacologic and nonpharmacologic interventions than non-VHA veterans. Differences exist in the processes of MS bladder care between the VHA and elsewhere; this variation suggests the availability of opportunities to optimize such care.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".