Are lower TSH cutoffs in neonatal screening for congenital hypothyroidism warranted?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it