Naloxone dosing: An evolving unregulated drug landscape, care setting considerations, and the need for research
Bibliographic record
Abstract
than naloxone, it could reduce the risk of re-narcotization, particularly in rural and remote areas with delayed access to medical care.However, the potential for prolonged withdrawal symptoms, limited clinical data and lack of global availability warrant further investigation before broader adoption [6].Challenges in naloxone dosing parallel those seen in opioid agonist therapy in the HPSO era [7][8][9].Both domains are marked by limited evidence, an unpredictable drug supply and the need for individualized approaches.Addressing these gaps requires well-designed clinical trials and translational research.In the absence of definitive data, collaboration among clinicians, researchers, policymakers and people with lived or living experience is essential to developing pragmatic, equitable and evidence-informed naloxone strategies.A coordinated and adaptive response is needed to address the urgency of this evolving public health crisis.
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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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".