A guide for urogynecologic patient care utilizing telemedicine during the COVID-19 pandemic: review of existing evidence
Bibliographic record
Abstract
INTRODUCTION AND HYPOTHESIS: The COVID-19 pandemic and the desire to "flatten the curve" of transmission have significantly affected the way providers care for patients. Female Pelvic Medicine and Reconstructive Surgeons (FPMRS) must provide high quality of care through remote access such as telemedicine. No clear guidelines exist on the use of telemedicine in FPMRS. Using expedited literature review methodology, we provide guidance regarding management of common outpatient urogynecology scenarios during the pandemic. METHODS: We grouped FPMRS conditions into those in which virtual management differs from direct in-person visits and conditions in which treatment would emphasize behavioral and conservative counseling but not deviate from current management paradigms. We conducted expedited literature review on four topics (telemedicine in FPMRS, pessary management, urinary tract infections, urinary retention) and addressed four other topics (urinary incontinence, prolapse, fecal incontinence, defecatory dysfunction) based on existing systematic reviews and guidelines. We further compiled expert consensus regarding management of FPMRS patients in the virtual setting, scenarios when in-person visits are necessary, symptoms that should alert providers, and specific considerations for FPMRS patients with suspected or confirmed COVID-19. RESULTS: Behavioral, medical, and conservative management will be valuable as first-line virtual treatments. Certain situations will require different treatments in the virtual setting while others will require an in-person visit despite the risks of COVID-19 transmission. CONCLUSIONS: We have presented guidance for treating FPMRS conditions via telemedicine based on rapid literature review and expert consensus and presented it in a format that can be actively referenced.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".