Bibliographic record
Abstract
The aim – to establish the prevalence of pain in patients with multiple sclerosis. Patients and methods. The patients, treated due to MS in the department of neurology in one year period were included in the study. We used original questionnaire, which was prepared joining McGill questionnaire, visual analog pain intensity measuring scale (VAS) and questions about the duration, treatment and adjacent diseases. Results. We had 47 patients. The age ranged between 22 and 63 years. 13 of them were male, 34 - female. There were no adjacent diseases in the group. 25 (53,2%) patients indicated that they are suffering from pain. More than half of them had backache, 36% had pain in the joints, mostly surrounding pain in the knee and wrist joints. 44% of the patients had face pain and headache. The patients themselves indicated the pain using pain descriptors (according McGill). Sensoric pain descriptors were used in 79% of cases, emotional – in 21%. The pain from slight to moderate (3-6 points) was in 74%, severe pain (8-9 points) was in 8% of the patients. Backache was constant and permanent (more than 1 year), the face pain – mostly unilateral and paroxysmal and had duration less than 6 months. Conclusions. More than half of the patients with MS suffer from the pain. The backache and sensoric type of pain prevailed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".