Dorsal perforation of prepuce: a common end point of severe ulcerative genital diseases?
Bibliographic record
Abstract
<h3>Objective:</h3> To describe the characteristics and outcomes of patients with relapsing-remitting multiple sclerosis (RRMS) not treated with disease-modifying therapies (DMTs). <h3>Background:</h3> People with RRMS who decline or delay DMT have been poorly characterized. In southern Alberta, MS patients are managed in a centralized MS clinic. DMTs are publicly funded for those with active RRMS, defined as having at least 2 relapses, or 1 relapse and an MRI enhancing lesion, within the previous 2 years. The goals of this study are to describe the untreated RRMS population. <h3>Design/Methods:</h3> As part of an ongoing prospective study of MS, a cohort of patients was defined who had RRMS and an EDSS score <5 at a baseline MS clinic visit during the period 1999–2006 and who had at least one year of follow-up during which they did not initiate DMT. MS course, date of MS onset, sex, age, EDSS scores, and DMT use are captured prospectively in administrative databases. MRI lesions, relapses, and reasons for declining DMT were captured retrospectively by chart review. <h3>Results:</h3> Of 522 eligible patients, 394 consenting participants (75.5%) were included in the study cohort. Women comprise 81% (n=320) of the cohort. Mean (SD) age was 31.0 (8.9) years at MS onset and 40.6 (9.5) years at baseline. The median (IQR) baseline EDSS was 2.0 (1.5, 2.5). DMT eligibility criteria were met by 140 (35.5%) participants at baseline. Within the previous 2 years 289 (73.4%) had at least one relapse. Among 172 (43.7%) participants who started DMT, median (IQR) time to DMT initiation was 2.4 (1.5, 5.2) years. Consenting participants did not differ from non-consenting patients. Time to DMT eligibility, EDSS change, and comparison with a contemporaneous DMT-treated cohort will be reported. <h3>Conclusions:</h3> This study describes the characteristics and long-term outcomes of untreated RRMS patients. <b>Disclosure:</b> Dr. Al Sultan has nothing to disclose. Dr. Parpal has nothing to disclose. Dr. Lavorato has nothing to disclose. Dr. Greenfield has nothing to disclose. Dr. Metz has nothing to disclose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".