Inter- and Intra-rater Reliability of the Checketts’ Grading System for Pin-site Infections across All Skin Colours
Bibliographic record
Abstract
The Checketts' grading system (CGS) is the only classification that provides both a description of how to visually grade the infection and the appropriate course of treatment. There are no studies on the reliability of this system nor on whether skin colour can influence applicability. This study aims to determine the inter-rater and intra-rater reliability of the CGS to assess whether this scale could be used as a universal grading system across all skin colours. A survey consisting of 134 anonymised photographs of pin-site infections was sent out to orthopaedic surgeons specialising in limb lengthening and reconstruction and to patients or carers of individuals who had external fixators. For each photograph, the participants were asked to grade the infection using the CGS, rate their confidence in their chosen grade on a Likert scale and assign a treatment option. The participants were supplied with the CGS at the beginning of the survey, after the 45th and 90th photographs. The inter-rater reliability of the CGS between the surgeons, expressed as an intraclass correlation coefficient (ICC), was poor-to-moderate at both time points (ICC = 0.56 for baseline survey and ICC = 0.48 for follow-up). This was similar for the patient or caretaker group. There was a lower inter-rater reliability for grading of dark skin as opposed to light skin by surgeons but not for patients or caretakers. The inter-rater reliability of treatment decisions between the surgeons was poor at both time points (kappa = 0.30 and 0.22) with similar inter-rater reliability for dark (kappa = 0.26 and 0.23) compared with light skin (kappa = 0.29 and 2.6). This was similar for the patient or caretaker group. The surgeons' confidence (Table 4) in grading was low (median = 1). The patient or caretaker group's confidence in their grading was modest (median = 2). The reliability of the CGS as assessed here demonstrates poor-to-moderate inter-rater reliability which makes interpretation of published pin site infection rates using this scale difficult. The design of new grading systems will need to consider skin colour to reduce inequities in medical decision-making. How to cite this article: . Inter- and Intra-rater Reliability of the Checketts' Grading System for Pin Site Infections across All Skin Colours. Strategies Trauma Limb Reconstr 2023;18(1):2-6.
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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 | 0.051 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".