Estimating the Daily Milligrams of Morphine Equivalent of Illicit Fentanyl Use in Los Angeles: Clinical and Epidemiological Implications
Bibliographic record
Abstract
INTRODUCTION: The market shift from heroin to illicitly-manufactured-fentanyl in North America led to surging opioid mortality. However, limited information exists about the doses of illicit fentanyl regularly consumed. We examined purity of fentanyl samples and estimate the typical daily oral milligrams of morphine equivalent (MME). METHODS: Leveraging community-based drug checking data from Los Angeles, we ascertained the purity of 509 samples of fentanyl collected between September 2023 and January 2026 using liquid chromatography mass spectrometry. We assessed typical consumption quantity and routes of administration among 47 respondents who reported regularly using fentanyl. We estimate bioavailability and MME conversion factors from literature. To estimate daily MME, incorporating all parameter uncertainty, we used a bootstrapping approach with 1,000,000 draws, with sensitivity analyses to assess the impact of factors including the correlation between purity and quantity. RESULTS: Among participants, the mean daily consumption of fentanyl was 1.07 g (95% prediction interval: 0.03g-4.00g). Illicit fentanyl products had a mean fentanyl purity of 12.47% (0.23%-38.80%), and the mean estimated bioavailability based on routes of administration was 50.82% (30.64%-76.75%). The mean estimated IV fentanyl to PO morphine MME conversion factor was 1 to 183.15 (71.85-294.21). The mean estimated daily consumption in our sample was 8887.55 MME (156.56 MME-41,761.30 MME). CONCLUSIONS: Under all plausible estimation scenarios, individuals consuming illicit fentanyl in Los Angeles on average use a quantity of MME several orders of magnitude higher than clinical guidelines or typical methadone doses. This likely contributes to high overdose mortality, high opioid tolerance, and more difficult methadone and buprenorphine induction.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".