Are lower TSH cutoffs in neonatal screening for congenital hypothyroidism warranted?
Bibliographic record
Abstract
When newborn screening (NBS) for congenital hypothyroidism (CH) using thyroid-stimulating hormone (TSH) as a primary screening test was introduced, typical TSH screening cutoffs were 20-50 U/L of whole blood. Over the years, lowering of TSH cutoffs has contributed to an increased prevalence of detected CH. However, a consensus on the benefit deriving from lowering TSH cutoffs at screening is lacking. The present paper outlines arguments both for and against the lowering of TSH cutoffs at NBS. It includes a review of recently published evidence from Australia, Belgium and Italy. A section focused on economic implications of lowering TSH cutoffs is also provided. One issue that bears further examination is the extent to which mild iodine deficiency at the population level might affect the association of neonatal TSH values with cognitive and developmental outcomes. A debate on TSH cutoffs provides the opportunity to reflect on how to make NBS for CH more effective and to guarantee optimum neurocognitive development and a good quality of life to babies with mild as well as with severe CH. All authors of this debate article agree on the need to establish optimal TSH cutoffs for screening programs in various settings and to ensure the benefits of screening and access to care for newborns worldwide.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".