Objective:

To develop and independently validate an interpretable machine learning model to predict early acute postoperative hypocalcemia in patients with differentiated thyroid cancer.

Approach:

Key Findings:

• The incidence of early acute postoperative hypocalcemia was 38.2%.
• Logistic Regression achieved an area under the receiver operating characteristic curve of 0.760 in the test set.
• Key predictors identified through analysis included preoperative serum magnesium, body mass index, lateral cervical lymph node dissection, and parathyroid autotransplantation dynamics.

Interpretation:

Limitations:

• The study is retrospective and may be subject to biases, including selection and information bias.
• Findings may not be generalizable beyond the studied population.

Conclusion:

A transparent, highly discriminative LR-based nomogram was developed for predicting early acute postoperative hypocalcemia.

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