The inter-rater reliability of the diagnosis of surgical site infection in the context of a clinical trial
Bibliographic record
Abstract
OBJECTIVES: The diagnosis of surgical site infection following endoprosthetic reconstruction for bone tumours is frequently a subjective diagnosis. Large clinical trials use blinded Central Adjudication Committees (CACs) to minimise the variability and bias associated with assessing a clinical outcome. The aim of this study was to determine the level of inter-rater and intra-rater agreement in the diagnosis of surgical site infection in the context of a clinical trial. MATERIALS AND METHODS: The Prophylactic Antibiotic Regimens in Tumour Surgery (PARITY) trial CAC adjudicated 29 non-PARITY cases of lower extremity endoprosthetic reconstruction. The CAC members classified each case according to the Centers for Disease Control (CDC) criteria for surgical site infection (superficial, deep, or organ space). Combinatorial analysis was used to calculate the smallest CAC panel size required to maximise agreement. A final meeting was held to establish a consensus. RESULTS: Full or near consensus was reached in 20 of the 29 cases. The Fleiss kappa value was calculated as 0.44 (95% confidence interval (CI) 0.35 to 0.53), or moderate agreement. The greatest statistical agreement was observed in the outcome of no infection, 0.61 (95% CI 0.49 to 0.72, substantial agreement). Panelists reached a full consensus in 12 of 29 cases and near consensus in five of 29 cases when CDC criteria were used (superficial, deep or organ space). A stable maximum Fleiss kappa of 0.46 (95% CI 0.50 to 0.35) at CAC sizes greater than three members was obtained. CONCLUSIONS: There is substantial agreement among the members of the PARITY CAC regarding the presence or absence of surgical site infection. Agreement on the level of infection, however, is more challenging. Additional clinical information routinely collected by the prospective PARITY trial may improve the discriminatory capacity of the CAC in the parent study for the diagnosis of infection.Cite this article: J. Nuttall, N. Evaniew, P. Thornley, A. Griffin, B. Deheshi, T. O'Shea, J. Wunder, P. Ferguson, R. L. Randall, R. Turcotte, P. Schneider, P. McKay, M. Bhandari, M. Ghert. The inter-rater reliability of the diagnosis of surgical site infection in the context of a clinical trial. Bone Joint Res 2016;5:347-352. DOI: 10.1302/2046-3758.58.BJR-2016-0036.R1.
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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 | 0.530 | 0.598 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".