Diagnostic utility of a modified forearm ischemic exercise test and technical issues relevant to exercise testing
Bibliographic record
Abstract
The sensitivity and specificity of a modified forearm ischemic test (FIT) are described in the diagnosis of glycogen storage disease, myoadenylate deaminase deficiency, and mitochondrial disease. FIT and muscle biopsy results were reviewed from 99 patients (glycogen storage disease [GSD], myoadenylate deaminase deficiency [AMPD], mitochondrial disease [MITO], miscellaneous neuromuscular disorders, and controls). The influence of catheter placement and an antecedent sugar bolus were also assessed in healthy young men. The FIT had a sensitivity of 1.00 and a specificity of 1.00 for a diagnosis of GSD, whereas the corresponding values were 1.00 and 0.37 for AMPD deficiency. A baseline lactate of >2.5 mmol/L provided the highest sensitivity (0.62) and specificity (1.00) for MITO disease. A baseline and +1 min sample provided optimal sensitivity and specificity for GSD and AMPD deficiency. Catheter placement in any vein other than the ipsilateral antecubital resulted in attenuated lactate responses (P < 0.0001). A pre-FIT sugar bolus did not alter the postexercise lactate or ammonia response. Thus, a modified FIT was helpful in the diagnosis of GSD and excluding AMPD deficiency, but not in the diagnosis of MITO disease. Catheter placement is critical to the interpretation of a FIT, whereas pretesting diet is less important.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".