Is there a relationship between surgical volume and outcome for total elbow arthroplasty? A systematic review
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
Purpose: Total elbow arthroplasty (TEA) is rarely performed compared to other arthroplasties. For many surgical procedures, literature shows better outcomes when they are performed by experienced surgeons and in so-called 'high-volume' hospitals. We systematically reviewed the literature on the relationship between surgical volume and outcomes following TEA. Methods: A literature search was performed using the MEDLINE, EMBASE and CINAHL databases. The literature was systematically reviewed for original studies comparing TEA outcomes among hospitals or surgeons with different annual or career volumes. For each study, data were collected on study design, indications for TEA, number of included patients, implant types, cut-off values for volume, number and types of complications, revision rate and functional outcome measures. The methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale. Results: Two studies, which included a combined 2301 TEAs, found that higher surgeon volumes were associated with lower revision rates. The examined complication rates did not differ between high- and low-volume surgeons. In one study, low-hospital volume is associated with an increased risk of revision compared to high-volume hospitals, but for other complication types, no difference was found. Conclusions: Based on the results, the evidence suggests that high-volume centers have a lower revision rate in the long term. No minimum amount of procedures per year can be advised, as the included studies have different cut-off values between groups. As higher surgeon- and center-volume, (therefore presumably experience) appear to yield better outcomes, centralization of total elbow arthroplasty should be encouraged.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it