Patterns of Acute Gamma‐Hydroxybutyrate Harms Requiring Ambulance Attendance: Should Greater Focus Be on Regional Areas?
Bibliographic record
Abstract
INTRODUCTION: Gamma-hydroxybutyrate (GHB) use and attributable harms have been increasing in Europe and Australia. However, there are limited population surveillance tools available to map and track acute GHB-related harms, particularly outside metropolitan areas. The present study examined GHB-related ambulance attendances from January 2015 to March 2024 across the state of Victoria, and in Greater Geelong, the region associated with the highest number of attendances outside the state capital. METHODS: Retrospective analysis of all GHB-related ambulance attendances between 1 January 2015 and 31 March 2024 from the Victorian arm of the National Ambulance Surveillance System. Descriptives and time series analyses were used to present demographic and spatio-temporal patterns. RESULTS: There were 16,971 ambulance attendances for GHB during the study period. A sinusoidal trend was apparent in the statewide data, suggesting a seasonal factor to GHB-related attendances, with greater numbers occurring during quarter four of each year. Whilst a seasonal effect was also apparent in Greater Geelong, increases in attendances have been consistent since quarter four of 2021 (between 7% and 34%). The magnitude of these increases was not observed in other regional areas. DISCUSSION AND CONCLUSIONS: Acute GHB-related harms have increased in Victoria over time, in addition to a seasonal effect being apparent that coincided with summer in the Southern Hemisphere. Our findings support recent media reports from emergency department workers in the region of Greater Geelong that GHB harms have risen. This study demonstrates the value of using ambulance surveillance data to assess pre-hospital harms resulting from GHB use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".