Painful Prescriptions: Opioid and Antibiotic Use for Dental Pain in the Emergency Department
Bibliographic record
Abstract
Overprescription of opioids and antibiotics remains a significant public health challenge in the US, contributing to systemic and dental health issues. This study developed and tested a natural language processing (NLP) model to identify patients visiting the emergency department (ED) at Temple University Health System for dental-related reasons. We extracted data from EHR and EDR systems, yielding a cohort of 89,349 patients, including 2,918 (3%) with dental-related ED visits. Using gold-standard datasets created through manual annotation, the NLP model combined fuzzy matching and embedding-based algorithms, achieving 95% accuracy, 98% specificity, and 92% sensitivity. The cohort was evenly split by gender, predominantly African American/Black (57%), with most patients aged 20-40 years (54%), and the majority relying on Medicaid (36%) or Medicare (28%). Notably, 70% of patients received antibiotics, and 11% were prescribed opioids. This study demonstrated the high prevalence of antibiotics and opioid prescriptions for dental pain in ED settings.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".