Response to Letter to the Editor for “Achieving the Minimum Clinically Important Difference in Japanese Orthopaedic Association Score after Surgery for Degenerative Cervical Myelopathy: Predictive Factors and Impact on Patient-Reported Outcome Measures”

We sincerely thank the authors of the Letter for their thoughtful comments and their interest in our article, “Achieving the Minimum Clinically Important Difference in Japanese Orthopaedic Association Score After Surgery for Degenerative Cervical Myelopathy: Predictive Factors and Impact on Patient-Reported Outcome Measures” (Global Spine Journal, 2025). We highly value this opportunity to clarify methodological considerations and to further contextualize our findings. Below, we address the main points raised.

Definition of MCID

We fully agree that the definition of the minimum clinically important difference (MCID) is critical and that estimates may vary depending on anchor-based vs distribution-based methods, baseline severity, and instrument responsiveness. In our study, we adopted a threshold of ≥2.5 points in the JOA score, consistent with previously published validation work from a Japanese cohort. Specifically, Kato et al ¹ conducted a psychometric evaluation of the original JOA score in 101 patients with degenerative cervical myelopathy who underwent laminoplasty. Using anchor-based receiver operating characteristic (ROC) analyses with patient satisfaction as the external criterion, they determined that the MCID for the JOA score was 2.5 points, while the patient acceptable symptom state (PASS) was 14.5 and the MCID for the JOA recovery rate was 52.8%. These results provided empirical justification that a 2.5-point improvement in the JOA score represents the smallest change perceived as clinically meaningful by patients. Based on this established evidence, we selected 2.5 as the most appropriate threshold for our study population. While we did not perform additional anchor-based or sensitivity analyses, we acknowledge the importance of incorporating patient-centered anchors, such as the global impression of change, in future work. We agree that such approaches would enrich interpretability and further confirm the robustness of this threshold.

Model Development and Validation

We reported an area under the ROC curve of 0.775, which indicates good discriminatory ability. As the Letter correctly points out, internal validation (eg, bootstrap correction) and calibration analyses would enhance the robustness of our prediction model. Our primary aim, however, was to identify clinically interpretable predictors rather than to provide a fully validated prediction tool. To minimize collinearity, we carefully selected candidate variables based on univariate screening and clinical relevance. Nonetheless, we recognize that penalized regression methods and decision-curve analysis may offer greater stability and clinical utility. We view this as an important step for subsequent studies building upon our findings.

Handling of Missing Data

We acknowledge the concern regarding missing data. In our multicenter prospective cohort, the proportion of missingness was relatively low, and a complete-case analysis was adopted for feasibility. While we do not believe this substantially altered our conclusions, we recognize that multiple imputation with auxiliary variables would provide more rigorous handling of missingness and reduce potential bias. We agree that future work should incorporate transparent reporting of missing data management, along with sensitivity analyses, to improve methodological robustness.

Effect Modification

The Letter highlights the possibility of treatment effect modification, such as interactions between baseline severity and sagittal alignment or between MRI signal changes and surgical approach. We appreciate this suggestion. In the present study, we focused on main effects to avoid overfitting and to maintain interpretability. Nevertheless, we concur that interaction analyses may identify clinically relevant subgroups with differing probabilities of achieving MCID and thereby inform personalized surgical counseling. This represents an important direction for future investigations using larger datasets.

Dichotomization of Outcomes

We selected MCID achievement as a binary outcome because it is clinically intuitive and facilitates communication between clinicians and patients. At the same time, we agree that dichotomization may obscure meaningful gradations of recovery and reduce statistical power. Continuous analyses, responder curves, and cumulative distribution plots would provide a more nuanced depiction of postoperative outcomes. We appreciate this recommendation and consider it an important complement for future analyses to capture the full spectrum of patient recovery.

Conclusion

In conclusion, we thank the authors of the Letter for their constructive insights. Their comments underscore both the strengths and limitations of our study and highlight valuable directions for further methodological refinement. We believe our findings contribute to understanding predictors of clinically meaningful recovery after surgery for degenerative cervical myelopathy. We also agree that greater transparency in MCID derivation, more rigorous model validation, exploration of treatment effect modification, and complementary analyses of continuous outcomes will enhance the clinical applicability of this field. We hope that this exchange fosters continued dialogue and advances in outcome research for patients with this challenging condition.

ORCID iDs

Toshiki Okubo https://orcid.org/0000-0003-0912-7422

Narihito Nagoshi https://orcid.org/0000-0001-8267-5789

Takahiro Kitagawa https://orcid.org/0000-0001-6746-8546

Kazuya Kitamura https://orcid.org/0000-0003-0117-4152

Kazuki Takeda https://orcid.org/0000-0003-3857-4985

Kota Watanabe https://orcid.org/0000-0002-4830-4690

Informed Consent

Written informed consent was obtained from all participants prior to their inclusion in the study.

Ethical Approval

The study protocol was approved by the Institutional Review Board (approval number: 20180045).

Data Availability Statement

The datasets generated and/or analyzed in this study are available from the corresponding author on request.*