Bibliographic record
Abstract
Commentary “There are only two options: Make progress or make excuses.”1 (Tony Robbins) Current Procedural Terminology (CPT) codes are sex, race, and ethnicity-blind. The finding by Holliday et al. that Medicare pays surgeons equally for total hip and knee replacement regardless of sex is not a surprise. Furthermore, commercial contracts for professional fee payments are not negotiated with different payment scales based on the personal characteristics of the orthopaedic surgeon. The full story of compensation, however, often is not told just by CPT codes. Different factors may contribute to the determination of a surgeon’s compensation, particularly at an academic medical center. Data from Jena et al. regarding physician salary in U.S. public medical schools that were adjusted for multiple factors (e.g., faculty rank, age, years since residency, specialty, publication count, total Medicare payments as a surrogate for clinical volume, etc.) confirmed a gender gap in compensation in orthopaedics, with men earning $368,070 and women earning $327,1172. Over the span of a career, this gap could total >$1,000,000, a substantial amount toward retirement security. Reasons for this gap are unclear and certainly multifactorial. Although as a society we believe that it is simply wrong for people to be paid differently for the same work, there may not be equal pay for equal work in orthopaedics, at least in academic medical centers. I am unaware of any data on gender analysis of compensation for orthopaedic surgeons in private practice. While the compensation question is an important one, the larger issue that the Holliday et al. paper raises is the substantial gender disparities in orthopaedics. We simply do not have enough women in the profession, and certain subspecialties are markedly underrepresented by women. Despite medical schools essentially reaching gender parity in 2001, orthopaedics had the third lowest percentage of women in residency programs (10.9%) in 2005, and the lowest percentage (14.8%) in 20153. Every other surgical specialty trains a higher percentage of women than orthopaedics. Moreover, our residency programs do not train women at equal rates: in the years 2009 to 2014, 30 programs had no female trainees4. Relative to subspecialty practice according to the American Academy of Orthopaedic Surgeons (AAOS) 2016 Orthopaedic Practice Survey, women self-report as orthopaedic specialists most commonly in pediatrics (22.1%), pediatric spine (15.6%), oncology (14.8%), and hand (13.5%); they self-report as specialists least commonly in adult spine (3.3%) and total joints (3.2%). Hence, the data in the Holliday et al. paper stating that only 1.9% of orthopaedic surgeons who submitted >10 Medicare claims for total knee arthroplasty (TKA) and 1.4% of orthopaedic surgeons who submitted >10 Medicare claims for total hip arthroplasty (THA) in 2013 were female are consistent with the AAOS data, and beg the question of whether we are encouraging (or discouraging) our women residents to pursue adult reconstructive fellowships. Our options are to make progress or make excuses. As Chair of the AAOS Diversity Advisory Board, I have repeatedly listened to the results of the AAOS surveys that asked fellows to rank priorities to guide AAOS leadership in allocation of limited resources. Diversity is never ranked as a priority by the majority of the fellows. We either don’t care (which I don’t believe), or don’t understand why diversity matters (which I do believe). Diversity of individuals and perspectives makes teams stronger and outcomes better. This is why we shouldn’t just train smart Caucasian males. There is ample evidence in the business world that companies with greater gender representation on executive boards are more financially successful; we intuit that these companies are making better decisions. Broader diversity of members mitigates the unconscious bias that each of us has as human beings. Unconscious bias is omnipresent in medicine as well—from the evaluation of physicians for compensation increases to surgical decision-making and patient compliance. While various studies in business have examined whether women ask or do not ask for increased compensation, the reality is that there is a compensation gap in business and, as previously discussed, with orthopaedic surgeons at academic medical centers. Structuring compensation on transparent objective metrics may improve compensation equity. Of greater concern to patients is the influence of unconscious bias on treatment recommendations. Women have more functional impairment and worse pain than men at the time of TKA, and postoperative function is not as favorable in women5. One reason may be that orthopaedic surgeons do not offer surgery to women at the same stage of disease, with women undergoing surgery when the disease is more severe. A fascinating study conducted in Ontario, Canada, utilized a standardized male and a standardized female patient with moderate knee osteoarthritis to gauge the recommendation for TKA. Borkhoff et al. showed that the odds of a family practice physician recommending TKA to the standardized male patient was 2 times that for the standardized female patient, and the odds of an orthopaedic surgeon recommending TKA to the male patient was 22 times that for the female patient6. There were insufficient numbers of female physicians in the Borkhoff et al. study to analyze if the gender of the provider impacted the results. How do we interpret these findings? Could it be that we, as orthopaedic surgeons (both women and men), believe that a man is more symptomatic than a woman, even if each describes the same level of pain and functional limitation as the standardized patients did in the Borkhoff et al. study? After all, we live in a society that has biased us to believe that women more readily voice their symptoms and have a lower pain threshold than men. And then there are the potential biases that we have relative to patients of color and to those with lower socioeconomic means. We must come to realize that our biases impact our patients. Clearly, additional research is needed to better understand the impact of gender, as well as race and ethnicity, on both physicians and patients regarding treatment recommendations and outcomes. Our nation is becoming more diverse. By 2042, the majority of the United States population will no longer be Caucasian. Disparities in health care are real. Orthopaedic surgery must become a profession that welcomes gender, racial, and ethnic diversity in order to attract the best and the brightest medical students and to best serve all of our patients.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.025 | 0.039 |
| Insufficient payload (model declined to judge) | 0.040 | 0.021 |
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".