Building Prospective Data Records for Adult Revision ACL Research

Authors

  • Zachary Ward Author
  • Oliver Franklin Author
  • Henry Dawson Author

Keywords:

prospective registry, graft healing, data provenance, outcome dictionary, longitudinal research, reproducibility

Abstract

A prospective revision ACL registry can support research only when it preserves treatment versions, outcome definitions, timing, selection, and reasons for missing observations. A large row count cannot supply those distinctions after they have been discarded. This conceptual design article develops a minimum record around an adult revision-management consensus. It links patient episodes, graft constructs, procedures, imaging, repeated outcomes, and subsequent treatment through versioned identifiers. Source observations remain distinguishable from adjudicated labels and derived variables. A defined knee instrument illustrates the need for a stable outcome dictionary, while methodological guidance separates reproducibility, bias assessment, and precision. No registry is established and no patient records are analyzed. The contribution is a feasible information architecture that can make later comparative and prognostic studies more auditable without treating registry scale as proof of clinical effectiveness.

Archive transfer

Originally published in Inclusive Growth and Management Quarterly (NB), article 218, on 2026-10-25. Transferred to Studies in Evidence and Discovery on 3 November 2026. The original authors, publication date and article text are retained.

References

  1. Cao, Mingde, et al. "APKASS 2024 consensus statement on anterior cruciate ligament reconstruction, Part II: Management of anterior cruciate ligament revision in adults." Asia-Pacific Journal of Sports Medicine, Arthroscopy, Rehabilitation and Technology 45 (2026): 74-81.
  2. Wang, J., Xu, G., Tong, H., Xu, G., & Chen, Y. (2026, May). Fractal Generative Distillation. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 20521-20525). IEEE.
  3. Wilkinson, Mark D., et al. "The FAIR Guiding Principles for Scientific Data Management and Stewardship." Scientific Data, vol. 3, 2016, article 160018. https://doi.org/10.1038/sdata.2016.18.
  4. von Elm, Erik, et al. "The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies." PLoS Medicine, vol. 4, no. 10, 2007, pp. e296. https://doi.org/10.1371/journal.pmed.0040296.
  5. Page, Matthew J, et al. "The PRISMA 2020 statement: an updated guideline for reporting systematic reviews." BMJ, 2021, pp. n71. https://doi.org/10.1136/bmj.n71.
  6. Sterne, Jonathan AC, et al. "ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions." BMJ, 2016, pp. i4919. https://doi.org/10.1136/bmj.i4919.
  7. Little, Roderick J. A., and Donald B. Rubin. Statistical Analysis with Missing Data. Wiley, 2002. https://doi.org/10.1002/9781119013563.
  8. Greenland, Sander, Judea Pearl, and James M. Robins. “Causal Diagrams for Epidemiologic Research.” Epidemiology, vol. 10, no. 1, 1999, pp. 37-48. https://doi.org/10.1097/00001648-199901000-00008.
  9. Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317–372. https://doi.org/10.3368/jhr.50.2.317.
  10. White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
  11. Irrgang, James J., et al. "Development and Validation of the International Knee Documentation Committee Subjective Knee Form." The American Journal of Sports Medicine, vol. 29, no. 5, 2001, pp. 600-613. https://doi.org/10.1177/03635465010290051301.

Downloads

Published

2026-10-25