A 15-year review of pediatric neurosurgical fellowships: implications for the pediatric neurosurgical workforce
Bibliographic record
Abstract
OBJECT: The Accreditation Council for Pediatric Neurosurgical Fellowships (ACPNF) was established in 1992 to oversee fellowship training in pediatric neurological surgery. The present study is a review of all graduates from 1992 through 2006 to identify predictors of American Board of Pediatric Neurological Surgery (ABPNS) certification. METHODS: Basic demographic information including sex, year of graduation from residency, residency training program, year of fellowship training, and fellowship program was collected on each graduate from each of the 22 ACPNF programs. Individuals who did not meet ACPNF requirements (39 trainees) and those currently practicing in Canada (11 individuals) were excluded. Univariate and multivariate analysis were used to identify predictors of ABPNS certification. RESULTS: Of the 193 ACPNF graduates, 143 individuals met the criteria for analysis. Currently, 70 (49%) are ABPNS certified. There is a mean period of 5.1 +/- 2.4 years (range 2-13 years) between finishing fellowship and ABPNS certification. If those who are not expected to be sitting for the boards yet (2002-2006 graduates, 57 individuals) are removed, the rate of ABPNS certification is 66.3%. On average, 9.5 +/-3.0 (range 4-16) fellows are trained per year. There is no statistically significant relationship between fellowship or residency training program and ABPNS certification. CONCLUSIONS: Although the present training infrastructure has the theoretical capacity to train > 20 pediatric neurosurgeons each year, this analysis suggests that current levels will provide approximately 6 ABPNS-certified pediatric neurosurgeons annually. This raises the question of the sufficiency of the future pediatric neurosurgical workforce.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".