ArticleBMJ open sport & exercise medicine2025
The Cambridge Knee Injury Tool (CamKIT): a clinical prediction tool for acute soft tissue knee injuries.
Article in BMJ open sport & exercise medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Feasibility study of the Cambridge knee injury tool (CamKIT): 18-month analysis of acute knee injuries at an urgent treatment centre.Journal of clinical orthopaedics and trauma · 2025Article
- Informing a Clinical Pathway for Acute Knee Injuries: Survey Insights From Rural Clinicians.The Australian journal of rural health · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/aim: This study focuses on the development of the Cambridge Knee Injury Tool (CamKIT), a clinical prediction tool developed as a 12-point scoring tool based on a modified e-Delphi study. Methods: A retrospective cohort evaluation was conducted involving 229 patients presenting to a Major Trauma Centre with acute knee pain over 3 months. The evaluation extracted data on the 12 scoring tool variables as well as diagnostic and management pathway outcomes. CamKIT scores for the injured and non-injured cohorts were then calculated and evaluated. Results: The CamKIT yielded a median score of 7.5 (IQR: 6-9) in the injured cohort, compared with a median score of 2 (IQR: 1-4) in the non-injured cohort, with a statistically significant difference (p<0.0001). When constructed as a three-tier risk stratification tool, the CamKIT produces a sensitivity of 100%, a specificity of 94.3%, a positive predictive value of 89% and a negative predictive value of 100% for diagnosing clinically significant soft tissue knee injuries. Conclusion: The CamKIT provides a non-invasive tool that has the potential to streamline the diagnostic process and empower healthcare workers in resource-stretched settings by instilling confidence and promoting accuracy in clinical decision-making. The CamKIT also has the potential to support efficiency in the secondary healthcare setting by enabling more targeted and timely use of specialist resources. This research contributes to the ongoing efforts to enhance patient outcomes and the overall quality of care in managing acute knee injuries.
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