Patterns of analgesic use to relieve tooth pain among residents in British Columbia, Canada
Bibliographic record
Abstract
The use of prescription opioids has increased dramatically in Canada in recent decades. This rise in opioid prescriptions has been accompanied by increasing rates of opioid-related abuse and addiction, creating serious public health challenges in British Columbia (BC), one of Canada's most populated provinces. Our study explores the relationship between dental pain and prescription opioid use among residents in BC. We used data from the 2003 Canadian Community Health Survey (CCHS), which asked respondents about their use of specific analgesic medications, including opioids, and their history of tooth pain in the past month. We used logistic regression, controlling for potential confounding variables, to identify the predictive value of socioeconomic factors, oral health-related variables, and dental care utilization indicators. The Relative Index of Inequality (RII) was calculated to assess the magnitude of socioeconomic inequalities in the use of particular analgesics by incorporating income-derived ridit values into a binary logistic regression model. Our results showed that conventional non-opioid based analgesics (such as aspirin or Tylenol) and opioids were more likely to be used by those who had experienced a toothache in the past month than those who did not report experiencing a toothache. The use of non-opioid painkillers to relieve tooth pain was associated with more recent and more frequent dental visits, better self-reported oral health, and a greater income. Conversely, a lower household income was associated with a preference for opioid use to relieve tooth pain. The RII for recent opioid use and conventional painkiller use were 2.06 (95% CI: 1.75-2.37) and 0.62 (95% CI: 0.35-0.91), respectively, among those who experienced recent tooth pain, suggesting that adverse socioeconomic conditions may influence the need for opioid analgesics to relieve dental pain. We conclude that programs and policies targeted at improving the dental health of the poor may help to reduce the use of prescription opioids, thereby narrowing health inequalities within the broader society.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".