How far are they coming from?
Bibliographic record
Abstract
INTRODUCTION: In Quebec, eight pediatric urologists practice in three tertiary centers covering large territories. To improve the availability of pediatric urology to distant families and to reduce the economic burden on them, we examined the charts of all patients attending the pediatric urological outpatient clinic. Our objectives were to evaluate the distance travelled by each urological pediatric outpatient and to report the most frequent urological referral complaints. METHODS: From July 2016 to June 2017, we retrospectively reviewed the charts of all the 3604 pediatric patients seen in the outpatient urological clinic of the CHU de Québec. We specifically focused on travel distance covered by families and the reason for referral. RESULTS: Most patients were boys (78%) and the mean age was 7.2 years. The average one-way distance travelled by each family was 69 km. The patients came more frequently from Capitale-Nationale (63.7%) and Chaudière-Appalaches (21.9%), the closest regions. The most common reasons for consultations were postoperative followups (15%), phimosis and adhesions (14%), enuresias (14%), hydronephrosis (13%), micturition disorder (11%), and cryptorchidism and retractile testicles (8%). Of all patients seen for phimosis or cryptorchidism, only 24% and 36% of them, respectively, were scheduled for surgery. CONCLUSIONS: Phimosis, cryptorchidism, and voiding disorders are the most frequent pediatric urological reasons for consultation; primary care continuing medical education seems worthwhile. It would, perhaps, be more beneficial for all to have the pediatric urologists travelling to perform clinics and surgeries in distant regions to save more than 300 km round trip to several families.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".