Bibliographic record
Abstract
Prescription opioids are an effective option to manage pain, but their role in clinical practice is complex due to their association with numerous potentially serious adverse events. The aim of this thesis was to assess the risk of opioid-related adverse events in three situations that cannot be easily examined using a randomized controlled trials in order to provide clinicians with additional guidance when considering this medication for their patients. This thesis included three population-based observational studies conducted using administrative data on individuals in Ontario, Canada who recently started prescription opioid therapy. Study one assessed the risk of motor vehicle collisions (MVC) among drivers starting prescription opioids compared to drivers starting prescription non-steroidal anti-inflammatory drugs (NSAIDs). There was no significant difference between exposure groups but the collision rates in both were higher than that for the general population, suggesting that pain may be a contributor to MVCs. The second study examined falls risk among older adults starting prescription opioids compared to prescription NSAIDs, at varying levels of concurrent central nervous system depressant burden. Those receiving opioids had a significantly higher risk of falling than those receiving NSAIDs regardless of the level of concurrent central nervous system depressant burden. Finally, the third study examined the risk of opioid toxicity, dose escalation, and new opioid use disorder (OUD) among adults with intellectual and developmental disabilities (IDD) compared to adults without IDD. Those with IDD had a significantly higher risk of opioid toxicity and OUD but a lower risk of dose escalation than those without in the unmatched analysis. However, these differences in risk were no longer significant once baseline differences in exposure groups were balanced through matching, suggesting that the IDD population is at a higher risk of opioid-related harms but this risk is not imposed by the IDD diagnosis. Rather, it is due to a clustering of known risk factors for opioid-related harms that are often present in the IDD population. Collectively, these results can be added to the evidence currently available to support clinicians and patients in their discussions to achieve safe and effective pain management.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".