Outcomes of hip and knee total joint arthroplasty in a Canadian inmate population over a 10-year period
Bibliographic record
Abstract
Background: Little information exists regarding the risk of complications in inmates who receive total hip or knee arthroplasties in Canada. Inmates tend to be less healthy owing to socioeconomic factors and an increased number of medical and psychiatric comorbidities. We compared revision and complication rates of total joint replacements in a cohort of incarcerated Canadians versus a cohort of non-inmates. Methods: We obtained a list of all Canadian inmate inpatient hospital visits with diagnostic/procedure codes of primary hip or knee arthroplasty within the last 10 years from our institution’s discharge abstract database. Demographic data and information related to the perioperative course, along with any data related to postoperative complications/readmissions, were obtained through manual chart review. Results: The inmate group consisted of 20 men (mean age 58 yr) with a total of 24 primary total joint arthroplasties; the comparison group included 171 men (mean age 62 yr). Postoperatively, the inmates had a 4-fold increased risk of major complication compared with non-inmates (33.3% v. 7.6%; odds ratio 4.21, p = 0.01). The inmates’ revision rate was 20.8% compared with 5.8% in the comparison group (p = 0.03). The most common cause for revision in the inmate group was infection, with a rate of 16.7% compared with 3.5% in the comparison group (p = 0.03). Conclusion: Patients requiring total joint arthroplasty who are inmates in the Canadian penitentiary system are at increased risk of complication and revision surgeries following total joint arthroplasty.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".