Economic Evaluation of Web-Based Compared with In-Person Follow-up After Total Joint Arthroplasty
Bibliographic record
Abstract
BACKGROUND: We previously demonstrated the feasibility and clinical effectiveness of a web-based assessment following total hip or total knee arthroplasty. The purpose of the present study was to conduct an economic evaluation to compare a web-based assessment with in-person follow-up. METHODS: Patients who had undergone total joint arthroplasty at least twelve months previously were randomized to complete a web-based follow-up or visit the clinic for the usual follow-up. We recorded travel costs and time associated with each option. We followed patients for one year after the web-based or in-person follow-up evaluation and documented any resource use related to the joint arthroplasty. We conducted cost analyses from the health-care payer (Ontario Ministry of Health and Long-Term Care) and societal perspectives. All costs are presented in 2012 Canadian dollars. RESULTS: A total of 229 patients (118 in the web-based group, 111 in the usual-care group) completed the study. The mean cost of the assessment from the societal perspective was $98 per patient for the web-based assessment and $162 per patient for the usual method of in-person follow-up. The cost for the web-based assessment was significantly lower from the societal perspective (mean difference, $-64; 95% confidence interval [CI], $-79 to $-48; p < 0.01) and also from the health-care payer perspective (mean difference, $-27; 95% CI, $-29 to $-25; p < 0.01). CONCLUSIONS: The web-based follow-up assessment had a lower cost per patient compared with in-person follow-up from both societal and health-care payer perspectives.
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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 | 0.026 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".