Accuracy of clinical symptoms and nuclear imaging in the diagnosis of infection in revision shoulder arthroplasty
Bibliographic record
Abstract
Prosthetic shoulder infection is the most common reason for revision arthroplasty within two years of the index procedure. Due to the preponderance of indolent and slow-growing infection in shoulder arthroplasty, establishing a diagnosis of infection prior to revision shoulder arthroplasty can be extremely difficult. The aim of this study was to determine the association between specific clinical symptoms and signs as well as nuclear imaging results with culture positive status in revision shoulder arthroplasty. This study is a secondary analysis of a multicenter prospective cohort study involving four sites and 69 patients who underwent revision shoulder arthroplasty. The cohort was 60% female with a mean age of 64 years (range 36-83 years). Outcomes of interest included preoperative specific clinical symptoms (pain at rest, fevers, chills and sweats); and white blood cell (WBC) nuclear scan results. At least five synovial tissue biopsies from separate regions in the shoulder were obtained intra-operatively during revision surgery. Twenty-three percent of patients (n=16) had confirmed culture positive infections based on the open biopsies. Sixty-four percent of patients presented with pain at rest. Pain at rest (p=0.37), chills (p=0.42), and sweats (p=0.11) were not associated with infection status. Fever was not reported by any patients in the cohort. There was no association between infection status and ESR (p=0.52), CRP (0.11), or WBC (p=0.34). No patients in the cohort had a positive WBC nuclear scan. This study suggests that specific clinical symptoms including pain at rest, systemic symptoms including fevers, chills, or sweats, and WBC bone scan are poorly associated with the presence of infection in revision shoulder arthroplasty. These findings point to the need for novel testing methods to establish the presence of infection in this patient population.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".