A Comparison of Various Data Reduction Procedures in a Multiple Sclerosis Sleep Study
Why this work is in the frame
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Bibliographic record
Abstract
Clinical studies often deal with datasets with numerous variables. As a result of the similarities between the variables, we frequently observe the presence of multicollinearity in the data. This study aimed to apply different data reduction strategies to sleep study variables in multiple sclerosis (MS) patients. The main objective was to use various data reduction strategies to explain a subjective measure of sleep quality (Pittsburgh Sleep Quality Index: PSQI) by the objective measures of sleep quality obtained during complete in-laboratory overnight polysomnography. Overall, we found that few objective measures of sleep quality were important in explaining the subjective PSQI, based on the results of various well-accepted statistical methods. Total sleep time was found to be the most important feature of objective sleep quality for explaining subjective sleep quality among all other investigated objective sleep quality variables in most of the approaches investigated in this study. The LASSO method for estimation worked best in terms of interpretability among all the approaches considered.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it