Impact of smartphone digital photography, email, and media communication on emergency room visits post-hypospadias repair
Bibliographic record
Abstract
INTRODUCTION: Advances in communication technology are shaping our medical practice. To date, there is no clear evidence that this mode of communication will have any effect on unnecessary postoperative emergency room (ER) visits. We aim to evaluate the effect of email and media communication with application of smartphone digital photography on post-hypospadias repair ER visit rates. METHODS: This prospective cohort study included all patients who underwent hypospadias repair performed by a single surgeon from October 2014 to November 2015. Patients were categorized into two groups: Group A consented for smartphone photography and email communication and Group B declined. Reason for ER visits within 30 days postoperatively was assessed by another physician, who was blinded of patient group assignment. The reasons were categorized as: unnecessary ER visit, indicated ER visit, or visit unrelated to hypospadias surgery. Chi-square test and T-test were used for statistical analysis. Relative risk (RR) and corresponding 95% confidence interval (CI) were also calculated. Statistical significance was set at p<0.05. RESULTS: Over a 14-month period, 96 patients underwent hypospadias repair (81 in Group A, 15 in Group B 5). No significant difference was noted between groups for overall ER return rate (RR 0.46, 95% CI 0.21, 1.0). However, the number of ER visits for wound check not requiring intervention was significantly lower in Group A than in Group B (RR 0.14, 95% CI 0.035, 0.56); likewise, a higher number of ER visits requiring intervention was noted in Group A compared with Group B, although statistically this was not significant (RR 1.67, 95% CI 0.23, 12.21). CONCLUSIONS: Email communication with the use of smartphone digital photography significantly reduced the number of unnecessary ER visits for post-hypospadias wound checks.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".