Screening and risk assessment for sleep-related breathing disorders in individuals living with spinal cord injury
Bibliographic record
Abstract
OBJECTIVES: This research work was performed: (1) To assess the accessibility of in-laboratory polysomnography for individuals with spinal cord injury (SCI); (2) to evaluate the validity of four screening questionnaires for sleep-related breathing disorders (SRBDs); and (3) to assess the association between anthropometric features and apnea-hypopnea index (AHI). METHODS: An Environmental scan (E-scan) was performed in the province of Ontario, where all sleep clinics were invited to complete the E-scan survey. Furthermore, a cross-sectional study was performed at a rehabilitation hospital (Canada), where consecutive adults with subacute/chronic (>1 month) SCI were recruited. Using a home-based screening sleep test (HBSST), the validity of the Berlin, STOP, Medical Outcomes Study Sleep Scale [MOS-SS], and STOP-Bang screening questionnaires was assessed. The association between AHI and three features (i.e., neck circumference, body mass index [BMI] and oropharynx opening as assessed using the Modified Mallampati classification [MMC]) was evaluated. RESULTS: According to the E-scan, access to polysomnography is limited for the SCI population in Ontario. Of the 28 participants with SCI (11 females, 17 males; mean age: 54.9 years) included in the cross-sectional study, 32.1 % were diagnosed with moderate-to-severe SRBD. The performance of the questionnaires was considered insufficient for screening of individuals living with SCI. AHI was not associated with neck circumference, BMI, or MMC. CONCLUSIONS: Those results suggest that the use of a HBSST could overcome the barriers for individuals with SCI to access diagnostic testing of SRBDs. The use of screening questionnaires and risk assessment for SRBDs in the SCI population is unreliable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".