The influence of calibration on bias in quality control and patient results for TSH on Vitros XT 7600 analyzer
Bibliographic record
Abstract
Introduction: Thyroid-stimulating hormone (TSH) is a glycoprotein secreted by the anterior pituitary gland and is regulated by negative feedback from the serum free thyroid hormones. In this study we aimed to quantitate the relative bias caused by calibration drifting as seen in our TSH Levey-Jennings quality control (QC) charts and assess the magnitude of bias on patients' samples. Materials and methods: In the period from October 2021 to August 2022 we looked at the QC results of ten 28-days' calibration time intervals and calculated relative bias compared to the mean. For each time interval the mean from three QC points before and after calibration was calculated. The average from 10 pre- and post-calibration means was calculated and the relative bias, pre- and post-calibration, was then calculated. We used 5 patient samples with low, normal and high TSH concentrations and calculated relative bias pre- and post-calibration. The allowed relative bias for TSH is ± 6.7%. Results: At both QC levels, with the respective means of 5.14 mIU/L (coefficient of variation, CV% = 3.1%) and 27.80 mIU/L (CV% = 3.2%) had their respective relative bias - 8.2% and - 7.9%. The patient samples with low (0.586 mIU/L), normal (2.89 mIU/L and 5.19 mIU/L) and high (20.5 mIU/L and 39.8 mIU/L) TSH had - 4.1%, - 4.0%, - 3.5%, - 5.1% and - 4.1%, respectively. Conclusion: Even though the relative bias exceeded allowable criteria for the QC samples, this was not manifested on the patients' samples.
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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 | 0.043 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".