A Comparison of Interobserver Reliability Between Orthopedic Surgeons Using the Centers for Disease Control Surgical Wound Class Definitions
Bibliographic record
Abstract
BACKGROUND: The Centers for Disease Control (CDC) created a classification to help stratify surgical wounds based on contamination and risk of developing a surgical site infection. The classification includes four options (I to IV) depending on the level of contamination present. Although universally applied to a variety of surgical specialties, it is unknown whether the current system is reliable when considering orthopaedic surgeries. The purpose of this study was to compare the degree of interobserver reliability between orthopaedic surgeons using the current CDC wound class definitions. METHODS: A questionnaire containing 30 clinical vignettes was completed by 39 orthopaedic surgeons at our institution. After each vignette, respondents were asked to determine the appropriate wound class based on information provided in the vignette. The overall interobserver agreement among all participants was analyzed. In addition, respondents were queried about the adequacy of the current classification system in describing orthopaedic surgical wound class. RESULTS: Interobserver agreement was poor at 66%, with a coefficient of concordance of 0.48. Only six physicians (15.4%) thought that the current wound classification system adequately covered orthopaedic surgery. CONCLUSIONS: There is poor interobserver reliability using the CDC surgical wound class definitions for orthopaedic surgeries. Alternate definitions are needed to improve the validity of the system for subspecialty procedures.
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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 | 0.082 | 0.179 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".