State-based Distribution of U.S. Pediatric Dentists in Private Practice.
Bibliographic record
Abstract
PURPOSE: To determine state-based and regional ratios of U.S. pediatric dentists to children in 2010 and determine changes since 2000. METHODS: State-based enumeration of pediatric dentists in private practice (PDP) was determined from the American Academy of Pediatric Dentistry's 2010 Membership Directory. Number of children in each state was obtained from U.S. Census 2010 data. PDP ratio for each state was computed per 100,000 children. Changes in state-based PDP number and ratio to children were compared with 2000 data. RESULTS: There were 4,453 pediatric dentists in private practice across the United States, with a ratio of 6.00 per 100,000 children. California (583), Texas (378), New York (310), and Florida (231) had the largest PDP numbers. The Southern region (1,609) had the largest PDP number, while the Midwest (706) had the lowest number. PDP ratio to children was highest in the Northeast (7.61) and lowest in the Midwest (4.38). With the exceptions of Alaska, Vermont, and Wyoming, the PDP number and its ratio to children increased in all states between 2000 and 2010. CONCLUSIONS: Despite a generalized increase in practitioner number since 2000, noticeable differences persisted in 2010 among states in their pediatric dentist to children ratios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".