Physiologic oxygen responses to smoking opioids: an observational study using continuous pulse oximetry at overdose prevention services in British Columbia, Canada
Bibliographic record
Abstract
Abstract Background In British Columbia, Canada, smoking is the most common modality of drug use among people who die of opioid toxicity. We aimed to assess oxygen saturation (SpO 2 ) while people smoked opioids during a pilot study that introduced continuous pulse oximetry at overdose prevention services (OPS) sites. Methods This was an observational cohort study, using a participatory design. We implemented our monitoring protocol from March to August 2021 at four OPS. We included adults (≥ 18 years) presenting to smoke opioids. A sensor taped to participants’ fingers transmitted real-time SpO 2 readings to a remote monitor viewed by OPS staff. Peer researchers collected baseline data and observed the timing of participants’ inhalations. We analyzed SpO 2 on a per-event basis. In mixed-effects logistic regression models, drop in minimum SpO 2 ≤ 90% in the current minute was our main outcome variable. Inhalation in that same minute was our main predictor. We also examined inhalation in the previous minute, cumulative inhalations, inhalation rate, demographics, co-morbidities, and substance use variables. Results We recorded 599 smoking events; 72.8% (436/599) had analyzable SpO 2 data. Participants’ mean age was 38.6 years (SD 11.3 years) and 73.1% were male. SpO 2 was highly variable within and between individuals. Drop in SpO 2 ≤ 90% was not significantly associated with inhalation in that same minute (OR: 1.2 [0.8–1.78], p = 0.261) or inhalation rate (OR 0.47 [0.20–1.10], p = 0.082). There was an association of SpO 2 drop with six cumulative inhalations (OR 3.38 [1.04–11.03], p = 0.043); this was not maintained ≥ 7 inhalations. Demographics, co-morbidities, and drug use variables were non-contributory. Conclusions Continuous pulse oximetry SpO 2 monitoring is a safe adjunct to monitoring people who smoke opioids at OPS. Our data reflect challenges of real-world monitoring, indicating that greater supports are needed for frontline responders at OPS. Inconsistent association between inhalations and SpO 2 suggests that complex factors (e.g., inhalation depth/duration, opioid tolerance, drug use setting) contribute to hypoxemia and overdose risk while people smoke opioids.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".