Utility of serum IGF-1 for diagnosis of growth hormone deficiency following traumatic brain injury and sport-related concussion
Bibliographic record
Abstract
BACKGROUND: Growth hormone deficiency (GHD) is a potential consequence of traumatic brain injury (TBI), including sport-related concussion (SRC). GH stimulation testing is required for definitive diagnosis; however, this is resource intensive and can be associated with adverse symptoms or risks. Measurement of serum IGF-1 is more practical and accessible, and pituitary tumour patients with hypopituitarism and low serum IGF-1 have been shown to have a high probability of GHD. We aimed to evaluate IGF-1 measurement for diagnosing GHD in our local TBI population. METHODS: We conducted a retrospective chart review of patients evaluated for GHD at the TBI clinic and referred for GH stimulation testing with insulin tolerance test (ITT) or glucagon stimulation test (GST) since December 2013. We obtained demographics, TBI severity, IGF-1, data pertaining to pituitary function, and GH stimulation results. IGF-1 values were used to calculate z-scores per age and gender specific reference ranges. Receiver operator curve analysis was performed to evaluate diagnostic threshold of IGF-1 z-score for determining GHD by GST or ITT. RESULTS: Sixty four patient charts were reviewed. 48 patients had mild, six had moderate, eight had severe TBI, and two had non-traumatic brain injuries. 47 patients underwent ITT or GST. 27 were confirmed to have GHD (peak hGH < 5 μg/L). IGF-1 level was within the age and gender specific reference range for all patients with confirmed GHD following GH stimulation testing. Only one patient had a baseline IGF-1 level below the age and gender specific reference range; this patient had a normal response to GH stimulation testing. ROC analysis showed IGF-1 z-score AUC f, confirming lack of diagnostic utility. CONCLUSION: Baseline IGF-1 is not a useful predictor of GHD in our local TBI population, and therefore has no value as a screening tool. TBI patients undergoing pituitary evaluation will require a dynamic test of GH reserve.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".