Testicular Torsion: An Analysis of Rural Geography and Socioeconomic Status
Bibliographic record
Abstract
ObjectivesTesticular torsion is a time-critical, organ-threatening diagnosis requiring prompt surgical intervention for successful salvage of the organ. In Australia, 28% of individuals live in rural and remote areas and face barriers to health care such as greater distance, lower socioeconomic status, (SES), and limited health infrastructure. We hypothesize that these barriers would delay intervention and access to surgical care, and lead to higher orchidectomy rates.ObjectivesA 12-year retrospective audit was conducted at a large rural referral center in Australia, focusing on patients undergoing scrotal exploration for testicular torsion. Primary outcomes were orchidectomy rate, time to operation, and ultrasound (US) and their relationship with patient distance, SES, age, and peripheral hospital attendance. Data on SES for geographic postcodes was obtained from the Australian Government Socio-Economic Indexes for Areas 2016. Statistical analysis was performed using IBM SPSS Statistics software, and a P value < 0.05 was considered significant.ResultsThe study involved 107 patients, of whom 46% had left-sided pathology. The median age of the patients was 14 years. Median SES was in the 37% to 41% centile range, median distance from travelled was 62 kilometers, and median time to operation from triage was 194 minutes. Of the patients, 34 attended a peripheral hospital. No significant risk factors for orchidectomy were identified. US was used in 65% of cases, with torsion detected in 50% of those cases, and orchidectomy performed in 11 patients. US had a sensitivity of 86.1% and specificity of 52.9%.ConclusionDespite significant differences in geographical distance, SES, age, and access to health care, patients in rural and remote areas of Australia experienced equivalent outcomes in testicular torsion management. Testicular torsion was safely managed at a central referral center using a peripheral hospital catchment in rural and remote areas of Australia, despite significant time delays due to greater distance or lower SES.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".