Screening for congenital hypothyroidism in newborns transferred to neonatal intensive care
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of four dried blood spot testing protocols used in newborn screening for congenital hypothyroidism (CH) among newborns transferred to the neonatal intensive care unit (NICU). DESIGN, SETTING AND PATIENTS: Michigan newborns transferred to the NICU from 1998 to 2011 and screened for CH are included in this population-based retrospective cohort study. MAIN OUTCOME MEASURES: Screening performance metrics are computed and logistic regression is used to test for differences in the likelihood of detection across four periods characterised by different testing protocols. RESULTS: Primary thyrotropin (TSH) plus retest at 30 days of life or discharge achieved the greatest detection rate (2.6: 1000 births screened). The odds of detection was also significantly greater in this period compared with the tandem thyroxine (T4) and TSH testing period and separately compared with TSH testing alone, adjusted for birth weight, sex and race (OR 1.5; CI 1.0 to 2.2; p=0.046, and OR 2.2; CI 1.5 to 3.4, respectively). Approximately half of the cases detected during primary TSH plus serial testing periods were identified by retest. CONCLUSIONS: Primary TSH testing programmes that do not incorporate serial screening may fail to identify approximately half of newborns with congenital thyroid hormone deficiency transferred to the NICU. Tandem T4 and TSH testing programmes also likely miss cases who otherwise would receive treatment if serial testing were conducted. Further research is necessary to determine the optimal newborn screening protocol for CH; strategies combining tandem T4 and TSH with serial testing conditional on birthweight may be useful.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".