Orthopaedic registries with patient-reported outcome measures
Bibliographic record
Abstract
Abstract Total joint arthroplasty is performed to decreased pain, restore function and productivity and improve quality of life. One-year implant survivorship following surgery is nearly 100%; however, self-reported satisfaction is 80% after total knee arthroplasty and 90% after total hip arthroplasty. Patient-reported outcomes (PROs) are produced by patients reporting on their own health status directly without interpretation from a surgeon or other medical professional; a PRO measure (PROM) is a tool, often a questionnaire, that measures different aspects of patient-related outcomes. Generic PROs are related to a patient’s general health and quality of life, whereas a specific PRO is focused on a particular disease, symptom or anatomical region. While revision surgery is the traditional endpoint of registries, it is blunt and likely insufficient as a measure of success; PROMs address this shortcoming by expanding beyond survival and measuring outcomes that are relevant to patients – relief of pain, restoration of function and improvement in quality of life. PROMs are increasing in use in many national and regional orthopaedic arthroplasty registries. PROMs data can provide important information on value-based care, support quality assurance and improvement initiatives, help refine surgical indications and may improve shared decision-making and surgical timing. There are several practical considerations that need to be considered when implementing PROMs collection, as the undertaking itself may be expensive, a burden to the patient, as well as being time and labour intensive. Cite this article: EFORT Open Rev 2019;4 DOI: 10.1302/2058-5241.4.180080
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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 | 0.061 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".