Bibliographic record
Abstract
BACKGROUND: Most outpatient orthopedic follow-up visits for patients who had total joint arthroplasty are routine among those with well-functioning implants. The technology and resources now exist to enable patient assessment without requiring attendance in hospital. We tested an electronic clinic for routine follow-up in a small cohort of arthroplasty patients. METHODS: We randomly assigned primary arthroplasty patients scheduled for routine annual outpatient review into 2 groups: group A completed a Web-based assessment 4 weeks after the clinical assessment, whereas group B completed the Web-based assessment first. Standard clinical questionnaires were included. We also collected radiographic data and information on assessment duration and cost. RESULTS: Forty patients participated in the study. The average age of participants was 58 years. There were 12 men and 8 women in each of the 2 groups. The average total time spent by patients on an outpatient visit was 115 minutes, compared with 52 minutes for the electronic assessment. Participants reported the electronic assessment to be more convenient and less costly. CONCLUSION: This pilot study supports the practical use of an electronic clinic for the follow-up of arthroplasty patients. Further studies examining the complex interaction of factors involved in patient clinics are needed.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".