Medical Malpractice in Nerve Injury of the Upper Extremity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Medical malpractice accounts for more than $55 billion of annual health care costs. Updated malpractice risk to surgeons and physicians related to upper extremity peripheral nerve injury has not been published. Methods: A comprehensive database analysis of upper extremity nerve injury claims between 1995 and 2014 in the United States was conducted using the Medical Professional Liability Association Data Sharing Project, representing 24 major insurance companies. Results: Nerve injury in the upper extremity accounted for 614 (0.3%) malpractice claims (total of 188 323). Common presenting diagnoses included carpal tunnel syndrome (41%), upper extremity fractures (19%), and traumatic nerve injuries to the shoulder or upper limb (8%). Improper performance (49% of total claims) and claims without evidence of medical error (19%) were the most common malpractice suits. Orthopedic surgeons were the most frequently targeted specialists (42%). In all, 65% of nerve injury claims originated from operative procedures in a hospital, 59% of claims were dismissed or withdrawn prior to trial, and 30% resulted in settlements. Thirty-three percent of claims resulted in an indemnity payment to an injured party, with an average payout of $203 592 per successful suit. Only 8% of claims resulted in a completed trial and verdict, and verdicts were overwhelmingly in favor of the defendant (83%). Conclusions: Most malpractice claims from peripheral nerve injuries in the United States arise from the management of common diagnoses, occur in the operating room, and allege improper performance. Strategies to reduce malpractice risk should emphasize the management of common conditions and patient-physician communication.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it