Access to hip arthroplasty and rates of complications in different socioeconomic groups
Bibliographic record
Abstract
Aims Total hip arthroplasty (THA) is one of the most successful surgical procedures. The objectives of this study were to define whether there is a correlation between socioeconomic status (SES) and surgical complications after elective primary unilateral THA, and investigate whether access to elective THA differs within SES groups. Methods We conducted a retrospective, population-based cohort study involving 202 hospitals in Ontario, Canada, over a 17-year period. Patients were divided into income quintiles based on postal codes as a proxy for personal economic status. Multivariable logistic regression models were then used to primarily assess the relationship between SES and surgical complications within one year of index THA. Results Of 111,359 patients who underwent elective primary THA, those in the lower SES groups had statistically significantly more comorbidities and statistically significantly more postoperative complications. While there was no increase in readmission rates within 90 days, there was a statistically significant difference in the primary and secondary outcomes including all revisions due (with a subset of deep wound infection and dislocation). Results showed that those in the higher SES groups had proportionally more cases performed than those in lower groups. Compared to the highest SES quintile, the lower groups had 61% of the number of hip arthroplasties performed. Conclusion Patients in lower socioeconomic groups have more comorbidities, fewer absolute number of cases performed, have their procedures performed in lower-volume centres, and ultimately have higher rates of complications. This lack of access and higher rates of complications is a “double hit” to those in lower SES groups, and indicates that we should be concentrating efforts to improve access to surgeons and hospitals where arthroplasty is routinely performed in high numbers. Even in a universal healthcare system where there are no penalties for complications such as readmission, there seems to be an inequality in the access to THA. Cite this article: Bone Joint J 2022;104-B(5):589–597.
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 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 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".