When dealing with a large dataset of electronic medical records (EMRs) and aiming to identify repeated musculoskeletal cases efficiently, several sampling methodologies and statistical approaches can be considered. Here are some recommendations:

1. Stratified Sampling:

- Stratified sampling involves dividing the dataset into homogeneous subgroups or strata based on relevant characteristics. In this case, you could stratify the EMRs based on the primary diagnosis or chief complaint, separating musculoskeletal cases from other conditions.

- Once stratified, you can then randomly sample from each stratum to ensure representation of musculoskeletal cases while also considering the variability in other conditions.

2. Random Sampling with Replacement:

- Random sampling with replacement involves randomly selecting cases from the dataset while allowing for the possibility of selecting the same case multiple times.

- This approach can be useful for identifying repeated musculoskeletal cases, as it allows for the inclusion of duplicate instances in the sample. By examining the frequency of occurrence of each case in the sample, you can identify patterns of repetition.

3. Cluster Sampling:

- Cluster sampling involves dividing the dataset into clusters, such as by provider, facility, or time period, and randomly selecting entire clusters for inclusion in the sample.

- In the context of identifying repeated musculoskeletal cases, you could cluster the EMRs based on the healthcare provider or clinic where the records originated. This approach can help capture patterns of repetition within specific healthcare settings.

4. Statistical Analysis for Duplicate Detection:

- Once you have obtained a sample of EMRs using the sampling methodology of your choice, you can employ statistical techniques for duplicate detection and identification of repeated cases.

- Methods such as data deduplication algorithms, record linkage techniques, and frequency analysis can help identify instances where the same musculoskeletal case appears multiple times within the sample.

5. Machine Learning Approaches:

- Machine learning algorithms, such as clustering algorithms and anomaly detection techniques, can be employed to identify patterns and anomalies within the dataset, including repeated musculoskeletal cases.

- By training machine learning models on the dataset, you can potentially automate the process of identifying and categorizing repeated cases based on similarities in patient demographics, diagnostic codes, or treatment patterns.

6. Cross-Validation Techniques:

- Cross-validation techniques, such as k-fold cross-validation, can be used to assess the performance of sampling methodologies and statistical approaches for identifying repeated musculoskeletal cases.

- By partitioning the dataset into training and validation sets and iteratively evaluating the effectiveness of different techniques, you can optimize the sampling process and ensure representative sampling of musculoskeletal cases.

a combination of stratified sampling, random sampling with replacement, cluster sampling, statistical analysis, machine learning approaches, and cross-validation techniques can be leveraged to efficiently identify and sort out repeated musculoskeletal cases within the dataset of electronic medical records. Depending on the specific characteristics of the dataset and the goals of the analysis, different methodologies may be more suitable for achieving accurate and representative sampling.