A Longitudinal Analysis of the Pediatric Surgeon Workforce
Bibliographic record
Abstract
OBJECTIVE: To describe the trends in the pediatric surgeon workforce during the last 25 years and to provide objective data useful for planning graduate medical education requirements. SUMMARY BACKGROUND DATA: In 1975, the Study on U.S. Surgical Services (SOSSUS) was published, including a model to survey staffing. A pediatric surgeon workforce study was initiated in conjunction with SOSSUS as a population, supply, and need-based study. The study has been updated every 5 years using the same study model, with the goals of determining the number and distribution of pediatric surgeons in the United States, the number needed and where, and the number of training programs and trainee output required to fill estimated staffing needs. This is the only such longitudinal workforce analysis of a surgical specialty. METHODS: Questionnaires were sent to 100 pediatric surgeons representing the 62 standard metropolitan statistical areas (SMSAs) in the United States with a population of 200,000 or more to verify the names and locations of all active pediatric surgeons and to gain information about the 5-year need for new pediatric surgeons by region. A program was developed to predict the number of pediatric surgeons relative to the total population and the 0-to-17-year-old population in the subsequent 30 years using updated data on the present number and ages of pediatric surgeons, age-specific death and retirement rates, projections of U.S. population by age group, and varying numbers of trainees graduated per year. As each 5-year update was done, previous projections were compared with actual numbers of pediatric surgeons found. The trends during the last 25 years were analyzed and compared and additional information regarding the demographics of practice, trends in reimbursement, and volume and scope of surgery was obtained. RESULTS: The birth rate has been stable since 1994. The 0-to-17-year-old population has been increasing at 0.65% per year; a 0.64% annual rate is projected to 2040. At present, 661 pediatric surgeons are distributed in every SMSA of 200,000 or more population, with an average age of 45 and an average age of retirement 65. The actual number of pediatric surgeons in each 5-year survey has consistently validated previous projections. Trainee output has increased markedly in the past 10 years. The rate of growth of the pediatric surgeon workforce at present is 50% greater than the forecasted rate of increase in the pediatric age group, and during the past 25 years the rate of growth of the pediatric surgeon workforce has been double that of the pediatric population growth. Nationally, significant changes in reimbursement, volume of surgery, and demographics of practice have occurred.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".