Bibliographic record
Abstract
Commentary Boakye et al. have written an excellent paper analyzing patients with foot and ankle conditions who had low baseline health, as measured by PROMIS outcome scores, using a large Boston-area database. They showed that patients who identified as Black or as having a Hispanic ethnicity had lower preoperative scores than those who did not. The sample size of 23,000 patients makes their database powerful. Nevertheless, smaller minority groups were not studied, as there was insufficient statistical power for appropriate analysis—an irony, as low representation defines a minority. The authors then took their analysis a step further by using a multiple regression approach to show that comorbidities, language, and education were underlying factors that were more relevant to health status than race and ethnicity were. Those involved in providing orthopaedic care to disadvantaged patients with foot and ankle conditions will find this intuitively obvious. Consider a disadvantaged patient with such a condition—in my practice, this may be a patient who is homeless or occupying a single room, someone who may have struggled with education, managed to get low-paying employment as a youth, perhaps had addiction issues, and had access to a poor diet in a rural community. Thirty years later, my patient has poorly controlled diabetes, is obese, is unable to work, and cannot afford medication, good nutrition, and transport. The paper by Boakye et al. assists me in understanding the barriers to care that are faced by my patient with comorbidities and poor education. The generalizability of a study—the applicability of the results found in the study population to other populations—needs to be considered. How does this paper relate to other areas in the U.S. outside Boston? How does it relate to Brazil, Canada, or Pakistan, as we begin to think more globally in provision of care? Science requires us to have valid and reliable terminology. What are valid and reliable terms for ethic groups, race, and language in each catchment area, and what races or ethnicities are in need? Clearly the mix of ethnicity and race in my practice in Canada is different from the study population. Census data show that the most common mother tongues of the residents in my Province, in order, are English (71%), Punjabi (4.4%), Cantonese (4.3%), Mandarin (4.1%), Tagalog (1.8%), German (1.5%), French (1.2%), and Korean (1.2%), all of which come before Spanish (1%)1. The population is 63% European, 11% Chinese, 8% South Asian, 3.2% Filipino, and 1% Black2. How can we apply the results of the study by Boakye et al. to a different population with a different ethnic, language, and racial mix? Surgeons will need to carefully interpret how the study’s results relate to their own populations. My group’s paper on differences in outcome scores in patients with end-stage ankle arthritis showed that women had lower preoperative and postoperative scores compared with men3. I was surprised that sex did not show as strong a role in the study by Boakye et al. This may reflect differences in the population studied as well as in health care and demographics between the U.S. and Canada. It would take a similar sample size and methodology in Canada to reach a meaningful conclusion. These issues involving health disparities and access to care have reached the attention of politicians. For example, equity of access to hip replacement is an issue in the U.K. National Health Service4. In most countries, health-care budgets and access are front and center in civic, state, and federal elections, as are disadvantaged populations. Our hospitals, surgical procedures, nurses, clinics, and reimbursement are often controlled by government bodies. The provision of care and access to care remain a major political issue. Provision of care requires us to provide science to support changes to allow equitable care. As surgeons and scientists, we can help by providing science and objectivity to the more subjective environment of politics and funding of health care. Involvement with international humanitarian causes will also help each of us better understand barriers to care, what local issues are, and what issues in health transcend all populations internationally. Boakye et al. also identified education as an issue that could have an impact on health in future years if it is addressed today. Dollar for dollar, childhood education is a much better investment than health care. Poor education results in poor health. It will take a generation at a minimum for change to take effect. In the meantime, we must do our imperfect best.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".