Sexual dysfunction damages: A legal database review
Bibliographic record
Abstract
INTRODUCTION: Procedural specialties are at higher risk for malpractice claims than non-procedural specialties. Previous studies have examined common damages and malpractice lawsuits resulting from specific procedures. Our goal was to analyze urological interventions that led to sexual dysfunction (SD) claims. METHODS: The Casetext legal research platform was queried using search terms for medical malpractice and common men's health procedures between 1993 and 2020. In total, 236 cases were found, and 21 cases met the inclusion criteria: malpractice cases against a urologist or urology group, clearly stated legal outcome, and allegation of sexual dysfunction from an intervention that directly caused damages. RESULTS: A total of 42 damages were cited in 21 lawsuits. The top three damages claimed were erectile dysfunction (ED) (14/42, 33.3%), genital pain syndrome (7/42, 16.7%), and urinary incontinence (5/42, 11.9%). The most commonly cited treatments were urinary catheter placement or removal (3/21, 14.3%), robotic-assisted laparoscopic radical prostatectomy (RALP) (3/21, 14.3%), circumcision (3/21, 14.3%), and penile implant (3/21, 14.3%). In 19 of 21 suits (90.4%), the outcome favored the defendant. Two cases favored the plaintiff: penile implant (failure to prove the patient was permanently, organically impotent prior to the procedure; missed urethral injury at time of surgery, $300 000) and vasectomy (damage to vasculature resulting in loss of testicle, $300 000). CONCLUSIONS: Most suspected malpractice cases resulting in SD favored the defendant urologist. Interestingly, urinary catheter placement is as likely to result in litigation as other operative interventions, such as RALP, inflatable penile prosthesis, and circumcision. It is possible that thorough preoperative counselling and increased responsiveness to patients' postoperative concerns may have avoided litigation in several cases.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.029 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".