Bibliographic record
Abstract
Abstract This chapter reviews classification schemes of sleep disorders and discusses assessment procedures commonly used to derive a diagnosis. Three different classifications, based predominantly on expert opinions and consensus, have evolved in parallel over the last few decades. Two of them are incorporated within larger taxonomies, such as the International Classification of Diseases (ICD) from the World Health Organization and the Diagnostic and Statistical Manual of Mental Disorders (DSM) from the American Psychiatric Association. A more specialized taxonomy, the International Classification of Sleep Disorders, has been developed by sleep medicine and research societies. Historically significant discrepancies have existed among these classification systems. However, increased collaboration among the nosologists charged with the development and refinement of these systems has made significant advances in increasing the concordance of these distinctive taxonomies. Yet the validity and reliability of the disorders listed in these classification schemes remain uneven, with some disorders supported by extensive empirical evidence and ascertained via reliable objective assays and others based primarily on consensus and identified largely by the questionably reliable clinical interview. Despite somewhat uneven evidence supporting the validity and reliability for the disorders these systems describe, they remain widely used by clinicians and researchers. Regardless of the system in use, accurate diagnosis is based on a multilevel assessment including a clinical interview, screening questionnaires and self-monitoring and in some cases laboratory procedures, and ambulatory behavioral assessment devices. Further research is needed to examine the validity and reliability of various sleep disorders and the clinical utility of current taxonomies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.106 | 0.054 |
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".