Orthopaedic registries with patient-reported outcome measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it