Effectiveness of McGill method at therapy of vertebrogenic algic syndrom.
Bibliographic record
Abstract
Title: The effectiveness of McGill's method in the treatment of Vertebrogenic algic syndrome. Objectives: The goal of this thesis was to determine the applicability of McGill's method to patients with diagnoses, that are collectively referred to as Vertebrogenic algic syndrome by comparing the measured data of proband with different locations of diagnosis of Vertebrogenic algic syndrome in the Czech Republic. Methods: This is a pilot experimental research involving 10 probands diagnosed with Vertebrogenic algic syndrome in the cervical spine, 10 probands diagnosed with Vertebrogenic algic syndrome in the thoracic spine and 10 probands diagnosed with Vertebrogenic algic syndrome in the lumbar spine. Each participant underwent a kinesiological examination according to the McGill's principles together with a SF-36 questionnaire. Measured values were compared and provided a basis for testing the hypotheses. The thesis uses methods of research, observation, querying and comparison of collected data. Results: Using the statistical analysis of the measured data demonstrated a positive effect of the McGill's treatment in terms of reduced pain in probands suffering from Vertebrogenic algic syndrome in all investigated locations. Comparing the measured data of individual groups shown that the difference in...
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".