B-114 Reference interval (RI) estimation for TSH in adults using an indirect method with unsupervised machine learning tool and R programming language
Bibliographic record
Abstract
Abstract Background Thyroid-stimulating hormone (TSH) plays a fundamental role in regulating thyroid hormones T3 (triiodothyronine) and T4 (thyroxine). Understanding TSH test results is essential for effective management of thyroid disorders and depends, among other factors, on establishing specific reference intervals (RI) for the population being served. We aimed to estimate the reference intervals for TSH in the adult population served by a large diagnostic network in Brazil. Methods We employed data mining followed by estimation using an indirect method through the LabRI™ algorithm with parametric, non-parametric, and robust statistical treatments for outlier exclusion, a series of algorithms programmed in R Language. Included were TSH values from outpatients, aged 20 to 60 years, both sexes, from two analytical platforms. Used as a comparative RI recommended by the American Thyroid Association (ATA, 2014). Data were extracted from the Data Lake, from January to December 2022, from two laboratories in Brazil. Exclusion criteria: samples from hospital sources and referral laboratories; pregnant; some laboratory tests correlated with de TSH test; samples from patients using thyroid hormone and medications that may interfere with hormonal function and TSH laboratory analysis. Results After exclusion criteria, the sample totaled 26,909 adults of both sexes. After excluding 2.24% outliers, the estimated RI was 0.67-4.26 mIU/L, with the comparative RI chosen for this study being the one recommended by ATA of 0.40-4.00 mIU/L. The RI obtained in our study was similar to the Canadian RI harmonization study for TSH (0.60-4.48 mIU/L). Conclusions We estimated the RI, harmonized between Roche and Siemens platforms, to be 0.67-4.26 mIU/L. The RI obtained by the LabRI™ tool is equivalent to the Adeli study (2023) and is under discussion by the company's endocrinology committee for whether to use the estimated RI from our internal study or the one recommended by ATA (2014) in reports.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".