Chronic exertional compartment syndrome (CECS) is a condition occurring most frequently in the lower limbs and often requires corrective surgery to alleviate symptoms. Amongst military personnel, the success rates of this surgery can be as low as 20%, presenting a challenge in determining whether surgery is worthwhile. In this study, the data of 132 fasciotomies for CECS was analysed and using combinatorial feature selection methods, coupled with input from clinicians, identified a set of key clinical features contributing to the occupational outcomes of surgery. Features were utilised to develop a machine learning model for predicting return-to-work outcomes 12-months post-surgery. An AUC of 0.85 ± 0.08 was achieved using a linear-SVM, trained using 6 features (height, mean arterial pressure, pre-surgical score on the exercise-induced leg pain questionnaire, time from initial presentation to surgery, and whether a patient had received a prior surgery for CECS). To facilitate trust and transparency, interrogation strategies were used to identify reasons why certain patients were misclassified, using instance hardness measures. Model interrogation revealed that patient difficulty was associated with an overlap in the clinical characteristics of surgical outcomes, which was best handled by XGBoost and SVM-based models. The methodology was compiled into a machine learning framework, termed AITIA, which can be applied to other clinical problems. AITIA extends the typical machine learning pipeline, integrating the proposed interrogation strategy, allowing to user to reason and decide whether to trust the developed model based on the sensibility of its decision-making.

An industrial standard steel ball plate of dimension 420 mm with 25 ceramic balls in a 5 × 5 arrangement was circulated among 12 European NMIs. The measurement task was the calibration of the centres of the 25 balls. The measurements were conducted between 2004 and 2006. One NMI withdrew from the comparison. The other 11 NMIs measured the ball plate and delivered data. The artifact was found to be stable during the duration of the comparison. For the analysis both the deviations from the KCRV and the En numbers were calculated. Most results agreed very well within the claimed uncertainty. A few results showed some larger deviations, e.g. length periodic modulations of the differences from the KCRV which still were within the claimed uncertainty, but might be a basis for some optimization of the measurement process. However, because of the significant length of time between the measurements and the final report, the optimization was already completed in most cases.Main text. To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/.The final report has been peer-reviewed and approved for publication by the CCL, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

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