Measurement of Insulin-Like Growth Factor-I During Military Operational Stress via a Filter Paper Blood Spot Assay
Bibliographic record
Abstract
Insulin-like growth factor-I (IGF-I) is sensitive to nutritional stress and is reduced in soldiers during stressful field training. Methods have recently been developed to measure IGF-I from filter paper blood spots. Filter paper has advantages over traditional blood sampling in that neither blood separation equipment nor refrigeration is necessary after sample collection. This study determined whether filter paper blood spots collected in a field environment could measure IGF-I and subsequent changes during military operational stress. Thirty-four Marines participating in an 8-day military field exercise characterized by near-continuous physical work (total daily energy expenditure 17-25 MJ/day) and underfeeding (dietary intake 7.0 MJ/day) had blood samples taken on day 0, day 4, and day 8. IGF-I was measured by filter paper blood spot assays from fingertip blood samples and by conventional methods using serum. Correlation and measurement agreement were assessed. Blood spot (Day 0 152 +/- 6 ng mL(-1) > Day 4 111 +/- 6 ng mL(-1) > Day 8 74 +/- 4 ng mL(-1)) and serum IGF-I (Day 0 412 +/- 10 ng mL(-1) > Day 4 258 +/- 14 ng mL(-1) > Day 8 203 +/- 13 ng mL(-1)) concentrations declined (p < 0.05) progressively over the 8-day exercise. Overall, the two methods significantly (p < 0.05) correlated (r = 0.92); however, the blood spot values were on average 61% lower than serum, but could be used to predict serum values ( +/- 10%). IGF-I is a biomarker of metabolic status. The filter paper blood spot method for IGF-I detected reductions accompanying nutritional stress and may be of potential value for characterizing the IGF-I response when conventional blood sampling methods are not feasible.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".