Bibliographic record
Abstract
OBJECTIVE: Local anesthetics are believed to be the most frequently used drugs in clinical dentistry, and although they are generally regarded as safe, some adverse reactions can be expected and do occur. The purpose of this study was to obtain, by means of a mail survey, information on the types and amounts of local anesthetics used by Ontario dentists during 2007. MATERIALS AND METHODS: A survey requesting data on the annual use of injectable local anesthetics was mailed to all 8,058 dentists licensed by the Royal College of Dental Surgeons of Ontario in 2007. RESULTS: The effective response rate to the single mailing was 17.3% (1,395 respondents). By extrapolation, the estimated use of local anesthetics by all Ontario dentists during 2007 was determined to be about 13 million cartridges, which represents an average of 1,613 cartridges per dentist per year. Lidocaine with epinephrine 1:100,000 was the most commonly used formulation with 37.31% of total anesthetic use, followed by articaine with 1:200,000 epinephrine (27.04%) and articaine with 1:100,000 epinephrine (17.16%). Overall, local anesthetics combined with a vasoconstrictor accounted for more than 90% of total anesthetic use. A minority of survey respondents (15.68%) indicated that their pattern of anesthetic use had changed significantly in the past few years. Patterns of use were similar for early and late survey respondents. These data provide a current account of the use of local anesthetics by Ontario dentists.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".