Prevalence of alcohol and other drug use in patients presenting to hospital for fall-related injuries: a systematic review
Bibliographic record
Abstract
BACKGROUND: Alcohol and other drug (AOD) use is a key preventable risk factor for serious injuries. Prevention strategies to date have largely focused on transport injuries, despite AOD use being a significant risk factor for other injury causes, including falls. This systematic review aimed to report the prevalence of AOD use in patients presenting to hospital for fall-related injuries. METHODS: This systematic review includes studies published in English after the year 2010 that objectively measured the prevalence of AOD use in patients presenting to hospital for a fall-related injury. Screening, data extraction and risk of bias assessments were completed by two independent reviewers. Data were presented using narrative synthesis and, where appropriate, meta-analyses. RESULTS: A total of 12 707 records were screened. Full texts were retrieved for 2042 records, of which 29 were included. Four studies reported the combined prevalence of any alcohol and/or drug use, generating a pooled prevalence estimate of 37% (95% CI 25% to 49%). Twenty-two records reported on the prevalence of acute alcohol use alone and nine reported specifically on the prevalence of drugs other than alcohol, with prevalence ranging from 2% to 57% and 7% to 46%, respectively. The variation in prevalence estimates likely resulted from differences in toxicology testing methods across studies. CONCLUSIONS: AOD exposure was common in hospitalised fall-related injuries. However, research addressing prevalence across different types of falls and the use of drugs other than alcohol was limited. Future research should address these areas to improve our understanding of which populations should be targeted in AOD and injury prevention strategies . PROSPERO REGISTRATION NUMBER: CRD42020188746.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".