Bibliographic record
Abstract
Introduction: Gamma-Butyrolactone (GBL) is a recreational drug, whose use has increased in recent years. However, it is highly addictive and when consumed in excessive amounts, physical and psychological dependence can develop. High profile deaths, including the death of a young medical student, have increased the public's awareness of GBL. However, in spite of this and a change in UK law (2009) to make the once legal high a class C drug, the number of patients presenting to emergency departments with GBL overdose has continued to rise. There is significant anxiety amongst professionals working in acute medicine and psychiatry when it comes to managing these difficult presentations, which combine symptoms of a frightening psychosis with marked physical compromise, which can ultimately lead to coma and death. There is also a lack of medical literature concerning GBL. Aim: To discuss a case that presented at Chelsea and Westminster Hospital, highlighting: The signs and symptoms of GBL intoxication and withdrawal. The importance of managing the psychosis alongside the physical symptoms. Where these patients should be managed. Case: 24-year-old patient, brought to A+E after police received reports about a gentleman in an underground station exhibiting ‘erratic, unusual behaviour’. Conclusion: This case highlights the need for an integrated approach to the management of substance misuse in acute hospital settings. GBL is a substance that most practising doctors have not been taught about. Education on the topic, and other emerging recreational drugs, should be an important part of our continuing professional development.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".