Bibliographic record
Abstract
The bachelor thesis deals with the topic of pain in patients with multiple sclerosis. The aim of the thesis is to map the prevalence of pain among patients with multiple sclerosis, its localization, time course, triggering factors, type, character, intensity and some other aspects pain through a shortened Czech form of the standardized questionnaire of McGill University. The thesis consists of theoretical and practical part. The theoretical part deals with the issue of pain in general, but also focuses on the individual types of pain that are characteristic of multiple sclerosis. The practical part relies on data collection by means of a questionnaire survey that focuses on mapping the prevalence of pain, its location, haracter, duration, intensity and other possible related factors. To collect data, I used a shorter form of the Czech version of the standardized McGill University questionnaire. The questionnaire survey was carried out with patients of the Centre for Demyelinating Diseases, Department of Neurology 1. LF UK and VFN in Prague, who were given a paper questionnaire. The information I obtained was processed into graphs and tables. The results showed that pain is present in 60 % of respondents with multiple sclerosis, most often localized in the spine and lower limbs. The intensity of...
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".