Relationship of Etiology to Treatment in Congenital Hypothyroidism
Bibliographic record
Abstract
We examined the patterns of TSH, T(4), and treatment schedules from diagnosis to 4 yr of age in 125 children (50 males anf 75 females) with congenital hypothyroidism (CH). Subjects were divided into 3 groups based on their thyroid scans: 1) athyreosis (n = 31), 2) dysgenesis (n = 54; 49 lingual and 5 hypoplastic), and 3) dyshormonogenesis (n = 40). Follow-up evaluation was carried out at 2-4 wk and 3, 6, 9, 12, 24, 36, and 48 months of age. Median gestational age, age at onset of therapy, and starting L-T(4) dose were similar in the three groups. In infants with athyreosis median screening TSH levels were higher (P < 0.02) and confirmatory T(4) levels were lower than in the other two groups (P < 0.01 vs. dysgenetic; P < 0.05 vs. dyshormonogenetic CH). During the first 6 months of therapy, mean TSH levels were highest in the athyrotic group, intermediate in the dysgenetic group, and lowest in the dyshormonogenetic group. In children with athyreosis, TSH levels normalized by 12 months of age. At 12 months dysgenetic patients had the highest TSH levels (P < 0.05). During the entire study period, TSH levels were lowest in patients with dyshormonogenesis (except at 48 months) and normalized earlier. Mean T(4) levels normalized by 2-4 weeks in all groups. At 3 and 6 months, the percentage of patients who required dose changes was highest in the athyrotic group, and at 12 months it was highest in the dysgenetic group. The athyrotic group received the highest dose of L-T(4), and dyshormonogenetic group received the lowest dose. We conclude that treatment and follow-up schedules for CH may differ in the three etiological categories based on the different hormonal patterns and responses to therapy. Children with athyreosis need close monitoring particularly early in life, whereas those with dysgenesis and dyshormonogenesis require more attention later in life.
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.
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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".