Oral and Maxillofacial Radiologists: Career Trends and Specialty Board Certification Status
Bibliographic record
Abstract
Oral and maxillofacial radiology is the newest specialty to be recognized by the American Dental Association, so knowledge about the parameters of this profession is in the early stages of development. The aim of this study was to understand the current distribution of oral and maxillofacial radiologists (OMFRs) in academia and private practice, the nature of their practice, and trends in their board certification status. An email describing the study's purpose with a link to a survey was sent to "OradList," a listserv that has a majority of OMFRs in the United States and Canada as members. Of the 205 respondents, 46% were female; the age distribution ranged from 25 to over 70 years; and 80% were working full-time. Among the respondents, 66% practiced in an academic setting, 20% in private practice, 8% in both private and academic settings, and 3% in the military. Only 37% of the respondents were board-certified. For OMFRs trained from 1965 to 2009, there was an increasing trend towards becoming board-certified, but a significant decrease occurred after 2009, dropping from 65% to 35% of those trained in those years.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".