A systematic analysis of ICSD-3 diagnostic criteria and proposal for further structured iteration
Bibliographic record
Abstract
The main objective of this theoretical review is to systematically analyze the type of International Classification of Sleep Disorders-3 (ICSD-3) diagnostic criteria by labeling each of them in order to propose an overview of the way in which the diagnostic criteria are organized. Labeling of diagnostic criteria using a rigorous iterative process of "aggregation" and "generalization" was conducted and inter-rater reliability calculation (Cohen's Kappa with three raters) was calculated. 241 criteria from 43 main sleep disorders of the ICSD-3 were labeled into nine types (Clinical manifestation 86.0% of sleep disorders, Objective markers 53.5%, Distress 30.2%, Disability 30.2%, Duration 30.2%, Frequency 58.1%, Age in 18.6%, Exclusion condition 81.4% and Associated condition 34.8%), with a high inter-rater reliability (Cohen's Kappa = 0.85). This analysis assumes that the structuring of the ICSD-3 diagnostic criteria is based on the Harmful Dysfunction Analysis (HDA). Some criteria correspond to the dysfunction part of the HDA while others refer to the harmful part. However, the approach does not seem to be homogeneous across the nosological classification. The use of a structured definition of sleep disorder and a framework to organize the ICSD diagnostic criteria is discussed with regard to the reliability and validity of criteria for diagnosing sleep disorders.
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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 | 0.155 | 0.273 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.024 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".