Surgeon Gender-Related Differences in Operative Coding in Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: Numerous studies in the medical and surgical literature have discussed the income gap between male and female physicians, but none has adequately accounted for the disparity. METHODS: This study was performed to determine whether gender-related billing and coding differences may be related to the income gap. A 10 percent minimum difference was set a priori as statistically significant. A cohort of 1036 candidates' 9-month case lists for the American Board of Plastic Surgery over a 5-year span (2014 to 2018) was evaluated for relationships between surgeon gender and work relative value units, coding information, major and minor cases performed, and work setting. Data were deidentified by the American Board of Plastic Surgery before evaluation. The authors hypothesized that work relative value units, average codes per case, major cases, and minor cases would be at least 10 percent higher for male than for female physicians. RESULTS: Significant differences were found between male and female surgeons in work relative value units billed, work relative value units billed per case, and the numbers of major cases performed. The average total work relative value units for male surgeons was 19.34 percent higher than for female surgeons [3253.2 (95 percent CI, 3090.5 to 3425.8) versus 2624.1 (95 percent CI, 2435.2 to 2829.6)]. Male surgeons performed 14.28 percent more major cases than female surgeons [77.6 percent (95 percent CI, 72.7 to 82.7 percent) versus 90.5 percent (95 percent CI, 86.3 to 94.9 percent); p = 0.0002]. CONCLUSIONS: The authors' findings support the hypothesis that billing and coding practices can, in part, account for income differences between male and female plastic surgeons. Potential explanations include practices focusing on larger and more complex operative cases and differences in coding practices.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".