Assessing Nephrology Competency in General Pediatrics
Bibliographic record
Abstract
Background: General pediatricians may be the first-line providers to care for children with kidney disease, however studies suggest they find nephrology to be a difficult subject. This study aimed to identify areas of lowest perceived competency and importance within nephrology for general pediatricians. Methods: A web-based survey was distributed to general pediatricians through the Paediatricians of Ontario network, to all Pediatrics Residency Program Directors in Canada and to Pediatric Nephrologists in the Canadian Association of Paediatric Nephrologists. Pediatricians were asked to rate nephrology objectives of training on a 5-point Likert scale for perceived competence and importance. Program Directors and Nephrologists were asked for perceived importance of each objective for general pediatricians. Scores were analyzed using Student’s t-test and mean scores were calculated. Knowledge Gap scores were calculated as the difference between perceived importance and competence scores. Results: General Paediatricians. 60/350 (17%) responded to the survey. Domains scoring significantly below the mean in terms of competency (2.9/5) and importance (3.2/5), respectively, were kidney stones (2.5 and 2.6), AKI (2.5 and 2.4), CKD (1.9 and 2.1), Tubular disorders (1.8 and 2.0), and kidney transplant (1.6 and 1.7). Hypertension had the most significant knowledge gap score (0.8/5, 16%, p<0.05). Program Directors. 9/17 (53%) responded to the survey. Nephrologists. 20/80 (25%) responded to the survey Program Directors and Nephrologists agreed that Stones, CKD, Tubular disorders, and Transplant were of lower importance. AKI was the domain with the largest discrepancy in perceived importance rated between nephrologists (4.2) and Program Directors (4.2) compared to general pediatricians (2.4) (1.8/5, 36%, p<0.05). Conclusions: General pediatricians do not feel comfortable with AKI and do not find this topic important to their practice, contrary to Program Directors’ and Nephrologists’ opinions, and despite growing evidence in recent years of the under-recognition, poor follow-up, and significant long-term implications of AKI. Hypertension is the area with the largest knowledge gap, which also raises concerns due to its rising prevalence in pediatrics. Educational interventions are needed to address deficits in these crucial domains of renal health in general pediatrics.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".