A prospective, multisite study analyzing the percentage of urological cases that can be completely managed by telemedicine
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has accelerated the development of telemedicine due to confinement measures. However, the percentage of outpatient urological cases that could be managed completely by telemedicine outside of the COVID-19 pandemic remains to be determined. We conducted a prospective, multisite study involving all urologists working in the region of Quebec City. METHODS: During the first four weeks of the regional confinement, 18 pediatric and adult urologists were asked to determine, after each telemedicine appointment, if it translated into a complete (CCM), incomplete (ICM), or suboptimal case management (SCM, adequate only in the context of the pandemic). RESULTS: A total of 1679 appointments representing all urological areas were registered. Overall, 67.6% (95% confidence interval [CI] 65.3; 69.8), 27.1% (25.0; 29.3), and 4.3% (3.5; 5.4) were reported as CCM, SCM, and ICM, respectively. The CCM ratio varied according to the reason for consultation, with cancer suspicion (52.9% [42.9; 62.8]) and pediatric reasons (38.0% [30.0; 46.6]) showing the lowest CCM percentages. CCM percentages also varied significantly based on the setting where it was performed, ranging from 61.1% (private clinic) to 86.8% (endourology and general hospital). CONCLUSIONS: We show that two-thirds of all urological outpatient cases could be completely managed by telemedicine outside of the pandemic. After the pandemic, it will be important to incorporate telemedicine as an alternative for a patient's first or followup visit, especially those with geographical, pathological, and socioeconomic considerations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".