Reporting of post-polyethylene glycol prolactin: precipitation by polyethylene glycol 6000 or polyethylene glycol 8000 will change reference intervals for monomeric prolactin
Bibliographic record
Abstract
BACKGROUND: When screening for macroprolactin, many laboratories use precipitation by polyethylene glycol (PEG) with molecular weight 6000 (PEG6000) or 8000 (PEG8000), and report the percentage prolactin recovery. It has been proposed that reporting of percentage prolactin recovery should be replaced by absolute post-PEG prolactin; however, the post-PEG prolactin reference interval has been established using PEG6000 only. We sought to determine whether the use of PEG8000, instead of PEG6000, changed post-PEG prolactin concentrations. METHODS: We compared the post-PEG6000 and post-PEG8000 prolactin concentrations in hyperprolactinaemic serum samples referred for macroprolactin screening (n=40), using Passing-Bablok regression analysis and Bland-Altman difference plot. RESULTS: The median (25th-75th percentile, range) total prolactin, post-PEG6000 and post-PEG8000 prolactin concentrations were, respectively, 36 (31-46, 23-83) μg/L, 27 (20-38, 18-72) μg/L and 24 (18-35, 16-64) μg/L for male serum samples (n=5); and 56 (39-83, 24-596) μg/L, 45 (31-67, 8-503) μg/L and 41 (28-62, 6-457) μg/L for female serum samples (n=35) (mIU/L conversion factor: 21.2). The Passing-Bablok analysis demonstrated a significant constant bias of -1.27 and a non-significant proportional bias of 0.96. The Bland-Altman plot showed a bias of -8.2% (95% limits of agreement -19.3-2.9%). CONCLUSIONS: There is a significant constant bias between the two macroprolactin precipitation methods. We changed our PEG precipitation to a PEG6000 method. Laboratories that use PEG8000 should consider the transference of the reference interval established with PEG6000 carefully.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".