MP66-07 TESTICULAR TORSION IN MINORS: DOES POINT-OF-CARE INFLUENCE TESTICULAR SALVAGE RATES? A POPULATION-BASED STUDY
Bibliographic record
Abstract
You have accessJournal of UrologyPediatrics: Testis, Varicocele & Stones1 Apr 2017MP66-07 TESTICULAR TORSION IN MINORS: DOES POINT-OF-CARE INFLUENCE TESTICULAR SALVAGE RATES? A POPULATION-BASED STUDY Katherine H Anderson, Bryan Maguire, Dawn L MacLellan, Peter AM Anderson, and Rodrigo LP Romao Katherine H AndersonKatherine H Anderson More articles by this author , Bryan MaguireBryan Maguire More articles by this author , Dawn L MacLellanDawn L MacLellan More articles by this author , Peter AM AndersonPeter AM Anderson More articles by this author , and Rodrigo LP RomaoRodrigo LP Romao More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2022AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Testicular torsion (TT) in minors is afflicted by delays in treatment due to inappropriate referrals to tertiary pediatric centers. We sought to determine whether point-of-care (community hospitals vs. tertiary centers) or other treatment delaying variables such as transfer, emergency room (ER) wait times and distance travelled affect testicular salvage rates in minors with TT using a National database. METHODS Data prospectively collected by the Canadian Institute of Health Information (CIHI) between January 2010-December 2014 were obtained; all Canadian males <18 years of age with TT based on ICD codes were included, except for the province of Quebec. Variables collected were: age, complexity level of surgical center based on case mix (community small/medium, community large, or tertiary/academic), if patient was transferred for definitive treatment, road distance travelled to the point-of-care based on postal codes, ER wait time in hours. Outcome was testicular salvage based on intervention codes used by CIHI for orchiectomy/orchidopexy. Uni and multivariate analyses were performed using logistic regression. RESULTS Complete data were available for 1736 out of 1935 TT patients <18 years of age. Overall testicular salvage rate was 70%. Most patients (52%) were treated at tertiary hospitals. On univariate analysis, there was no difference in testicular salvage rates between tertiary and large community hospitals (70% vs 66%); treatment at small/medium community hospitals was associated with higher salvage rates (77%) compared to large community ones (OR=0.59, CI 0.39-0.85, p<0.05). ER wait time longer than 1 hour was associated with a significant increase in testicular loss (OR=1.89, CI 1.41-2.52, p<0.0001). Transfer and distance travelled were not associated with higher orchiectomy rates, even on stratified analysis by type of hospital. On multivariate analysis, age 12-17 years, treatment at community small/medium or tertiary/academic hospitals and shorter ER wait times were significantly associated with higher salvage rates. CONCLUSIONS Point-of-care affects testicular salvage rates in minors with TT. Small/medium community hospitals depict the lowest orchiectomy rates; while academic centers had better outcomes than large community hospitals on multivariate analysis, it is unclear if this of clinical significance. Transfer to another facility for definitive care and distance travelled did not affect orchiectomy rates. Longer ER wait time and younger age were the most consistent risk factors associated with orchiectomy. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e864 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Katherine H Anderson More articles by this author Bryan Maguire More articles by this author Dawn L MacLellan More articles by this author Peter AM Anderson More articles by this author Rodrigo LP Romao More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".