The Canadian Joint Replacement Registry—what have we learned?
Bibliographic record
Abstract
The Canadian Joint Replacement Registry (CJRR) was launched in 2000 through the collaborative efforts of the Canadian Orthopedic Association and the Canadian Institutes for Health Information. Participation is voluntary, and data collected by participating surgeons in the operating room is linked to hospital stay information from administrative databases to compile yearly reports. In the fiscal year 2006-2007, there were 62,196 hospitalizations for hip and knee replacements in Canada, excluding Quebec. This represents a 10-year increase of 101% and a 1-year increase of 6%. Compared to men, Canadian women have higher age-adjusted rates per 105 for both TKA (148 vs. 110) and THA (86 vs. 76). There also exist substantial inter-provincial variations in both age-adjusted rates of arthroplasty and implant utilization that cannot be explained entirely on the basis of differing patient demographics. The reasons for these variations are unclear, but probably represent such factors as differences in provincial health expenditure, efforts to reduce waiting lists, and surgeon preference. The main challenge currently facing the CJRR is to increase procedure capture to > 90%. This is being pursued through a combination of efforts including simplification of the consent process, streamlining of the data collection form, and the production of customized reports with information that has direct clinical relevance for surgeons and administrators. As the CJRR continues to mature, we are optimistic that it will provide clinically important information on the wide range of factors that affect arthroplasty outcome.
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 | 0.078 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".