Bibliographic record
Abstract
Growth hormone (GH) is a powerful anabolic hormone with a broad spectrum of action that has been assessed with three general parameters: auxological to assess the growth response; biochemical to measure anabolic effects; and body composition. In childhood, linear growth response is assessed with height, short-term changes in height velocity (HV), and attainment of final adult height, which may not be concordant. In both children and adults, the biochemical indices utilized to predict and/or monitor response to GH therapy have included: (1) nonspecific indices: glucose, insulin, urea, protein synthesis, lipid metabolism, and lipoproteins; (2) more specific indices of the GH-IGF axis: GH binding protein, IGF-I, IGFBP-3, and acid-labile subunit; or (3) indices of bone and mineral metabolism: calcium, phosphate, bone alkaline phosphatase, osteocalcin, propeptides of procollagen type I and III, and bone mineral content. For body composition, body mass index, total body % fat, total body or extracellular water, and bone mineral density have been addressed most frequently. Modest changes with wide variability have been observed with most measurements. GH dose is a very significant positive factor for all parameters. Few of the currently available tests can reliably predict and/or monitor response to GH therapy. Of these, serum IGF-I appears to offer the best integrated indicator of the action of GH throughout all age groups.
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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 | 0.048 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".