ARE ROAD TRAFFIC ACCIDENTS PREVENTABLE AMONG SLEEP APNOEA PATIENTS?
Bibliographic record
Abstract
Background Drowsiness and lack of concentration among obstructive sleep apnoea (OSA) patients may strongly contribute to road traffic accidents (RTAs). Aims/Objectives/Purpose A meta-analysis was conducted in order to estimate whether RTAs can be prevented after treatment with continuous positive airway pressure (nCPAP) among (OSA) patients. Methods Real accidents, near miss accidents and accident-related events in the driving simulator were used as the primary outcomes after nCPAP treatment. Pooled ORs, incidence rate ratios (IRRs), standardised mean differences (SMDs), risk differences (RDs) and numbers needed to treat (NNTs) were appropriately calculated. Results/Outcome Concerning real accidents (10 studies, 1221 patients), a statistically significant reduction in RTAs was recorded (OR=0.21, 95% CI 0.12 to 0.35, random effects model; IRR=0.45, 95% CI 0.34 to 0.59, fixed effects model). A stronger reduction on near miss accidents (5 studies, 769 patients; OR=0.09, 95% CI 0.04 to 0.21, random effects model; IRR=0.23, 95% CI 0.08 to 0.67, random effects model) was also observed. With respect to the preventable fraction of RTAs, it was estimated that five (NNT=5, 95% CI 3 to 8) and two (NNT=2, 95% CI 1 to 4) OSA patients should be treated with nCPAP to prevent one patient reporting real and near miss road traffic accidents, respectively. Significance/Contribution to the Field It seems that nCPAP treatment may offer a sizeable protective effect upon RTAs prevention. The clinical role of health professionals in accident prevention should include identification of accident risks or medical conditions conferring risk (OSA), treatment of accident-causing conditions (nCPAP treatment) and reinforcement of advocacy and policy making towards promoting accident prevention education and training (OSA patients).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.022 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".