Bibliographic record
Abstract
Questionnaires to assess medical history, sleepiness and quality of life (QOL) are valuable tools to define the functional impact of the sleep-related breathing events in patients with obstructive sleep apnea syndrome (OSAS). If the answers of standardized history questionnaires are systematically evaluated, groups of different risk can be identified. Severity of symptoms together with anthropometric data predict with high sensitivity the probability of an increased apnea/hypopnea index. Sleepiness is a typical symptom of OSAS and can be characterized by subjective ratings, including the Stanford Sleepiness Scale and Epworth Sleepiness Scale. The Epworth Sleepiness Scale is most often applied in clinical routines because of its practicability. QOL is significantly impaired in OSAS patients. Evaluative instruments like the Medical Outcomes Study Short Form 36 (SF-36) measure multidimensional health components. The general questionnaires such as the SF-36 and Nottingham Health Profile were constructed to compare QOL of patients with different diseases with QOL of healthy subjects (discriminative property) and are not sensitive enough to changes. The Sleep Apnea Quality of Life Index and Quebec Sleep Questionnaire were developed to assess the specific effects of sleep apnea on QOL and within-subject changes after treatment. The choice of the instrument depends on whether sleepiness, impairment or treatment effects should be measured.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.305 | 0.132 |
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".