Background: Reference intervals for 25-hydroxyvitamin D are significantly influenced by seasonal variations and population-specific factors. This study aimed to establish local, population-specific Vitamin D reference intervals using the refineR algorithm and to compare these results with the conventional non-parametric method using a hospital database. Methods: A total of 127,220 laboratory results from 2022 to 2025 were retrospectively analyzed. Data were filtered based on adult age, morning fasting specimens, outpatient status, and normal parathyroid hormone levels (14–72 ng/L). Following filtration, Wilcoxon and Kruskal–Wallis tests were employed to evaluate differences between potential subgroups. Consequently, 24,036 eligible results were stratified by age (18–40, 41–65, and >65 years) and sex (female and male). Reference intervals were calculated using the refineR algorithm and the non-parametric percentile method. Results: The distribution of vitamin D levels was found to be right-skewed [Median: 12.92 ng/mL (4.21–149)]. The upper reference limits obtained via refineR were consistently lower than those derived from the non-parametric method. Both methods showed a linear increase in median values and upper reference limits with advancing age. Females in the 18–40 age group exhibited the lowest vitamin D profile. The summer concentrations were significantly higher than those of other seasons (p <0.05). Conclusions: The refineR algorithm managed outliers and pathological results more effectively than the non-parametric method. Our findings highlight the clinical necessity for age- and sex-specific subgroups, rather than relying on the manufacturer’s single reference range. Implementing these population-specific intervals can enhance diagnostic accuracy and prevent misclassification, facilitating earlier identification of vitamin D-related metabolic and skeletal disorders.

Purpose: Blood glucose measurement is of great importance in preventing complications and reducing mortality and morbidity for several diseases. Glucometers are used by any healthcare professionals. We conducted this study to show the compatibility between glucometers and autoanalyzers as a basis for hospital procurement. Materials and Methods: Fourty seven venous samples were used. Correlation, Passing Bablok regression analysis, and Bland-Altman difference plot were used to evaluate the results (glucometers and autoanalyzer). Clarke Error Grid analysis was used to assess the clinical agreement (p<0.05). Results: The correlation coefficient for G1 was 0.9294 (p<0.001; 95% CI: 0.8761-0.9603) and 0.9473 (p<0.001; 95% CI: 0.9069-0.9705) for G2. Regression equations were G1 y=1.1235x+3.4662 G2 y=0.8846x+10.0076. In Clarke Error Grid analysis, the criterion of 99% of the data being in zone A or B was met for both glucometers. Conclusion: According to the relevant document, based on the result obtained with the reference method as the target value, 95% of all results less than 100 mg/dl should be within ±12 mg/dl of the target value. 95% of all results above 100 mg/dl should be within ±12.5% of the target value. In addition, the ratio of the sum of results less than 75 mg/dl exceeding ±15 mg/dl and results greater than 75 mg/dl exceeding ±20% of the target value to all results should be less than 2%. Glucometers didn’t fully meet the performance targets. We think that glucometer analysis results should be closely monitored and verified after procurement.

Background: Thyroid function tests are frequently requested, but manufacturer reference intervals often lack age- or sex-based stratification. This study established indirect reference intervals for thyroid stimulating hormone (TSH), free thyroxine (fT4) and free triiodothyronine (fT3) in a large adult population. We evaluated unsupervised algorithms and conventional partitioning to identify subgroups, compared multiple limit estimation methods, and assessed the diagnostic performance of the derived intervals against an independent, clinically defined external validation cohort. Methods: Data from 37,255 adults (age ≥ 18) collected between 2022 and 2024 were analyzed; a reference population of 5870 individuals was established following standardized exclusion criteria. Age-based subgroups were identified through hierarchical clustering with the Elbow method, and variable importance was assessed using Random Forest analysis. Reference intervals were calculated using non-parametric, Bhattacharya, refineR, and reflimR algorithms, applied to three population frameworks: (1) the total population without stratification, (2) subgroups derived from hierarchical clustering and (3) subgroups defined by the conventional Harris–Boyd partitioning method. Diagnostic performance was subsequently evaluated in an independent external cohort (National Health and Nutrition Examination Survey [NHANES]; N = 2297) for all estimated reference intervals. Three classification scenarios were assessed: TSH-only, fT4-only, and combined TSH + fT4, with sensitivity, specificity, Youden index, and decision curve analysis performed for each. Results: Random Forest analysis identified age as the dominant variable influencing TSH, fT4 and fT3 distributions (mean decrease in accuracy: TSH 41.62, fT4 44.18, fT3 43.7), while sex showed the lowest impact. Clustering yielded six age-based subgroups for analytes. Harris–Boyd partitioning yielded six age-based subgroups for TSH, two sex-based subgroups for fT4, and six combined age-and-sex subgroups for fT3. TSH limits were broadly concordant across all three approaches (six-subgroup partitioning: 0.36–0.68 to 4.75–5.67 mIU/L). For fT4, conventional (sex-based) and clustering (age-based) partitioning produced similar ranges (11.33–20.08 pmol/L), except reflimR’s notably lower limit (10.90 pmol/L). For fT3, conventional (age + sex) and clustering (age-only) partitioning showed comparable ranges (3.36–7.03 pmol/L), with clustering revealing a clearer age-related decline in the oldest group. Diagnostic performance varied markedly by analyte. TSH-only classification achieved positive discrimination across all 13 methods (Youden index: 0.173–0.239). In contrast, fT4-only and combined TSH + fT4 classifications performed at or below chance for most methods, with 75% of the cohort falling outside fT4 reference intervals, indicating an inter-platform harmonization issue rather than a partitioning failure. Decision curve analysis confirmed TSH-only classification’s superiority, exceeding universal testing from pt ≈ 0.20 onward across all methods. Conclusions: Age-stratified reference intervals combined with limit estimation showed potential diagnostic advantages over manufacturer and non-stratified intervals; however, an independent external validation using a clinically defined outcome indicated that this advantage was not consistently reproduced and was dependent on the clinical decision threshold considered. These results suggest that age stratification and algorithm choice merit further clinically adjudicated validation before broad clinical adoption, and that unsupervised clustering offers a practical, objective alternative to manual subgrouping for laboratories pursuing this approach.