Identification of patients with Pompé disease using routine pathology results: PATHFINDER (creatine kinase) study
Bibliographic record
Abstract
AIMS: Adult-onset inherited errors of metabolism can be difficult to diagnose. Some cases of potentially treatable myopathy are caused by autosomal recessive acid α-1,4 glucosidase (acid maltase) deficiency (Pompé disease). This study investigated whether screening of asymptomatic patients with elevated creatine kinase (CK) could improve detection of Pompé disease. METHODS: Pathology databases in six hospitals were used to identify patients with elevated CK results (>2× upper limit of normal). Patients were recalled for measurement of acid α-1,4 glucosidase activity in dried blood spot samples. RESULTS: Samples were obtained from 812 patients with elevated CK. Low α-glucosidase activity was found in 13 patients (1.6%). Patients with neutropaenia (n=4) or who declined further testing (n=1) were excluded. Confirmation plasma specimens were obtained from eight individuals (1%) for a white cell lysosomal enzyme panel, and three (0.4%) were confirmed to have low α-1,4-glucosidase activity. One patient was identified as a heterozygous carrier of an acid α-1,4 glucosidase c.-32-13 G>T mutation. Screening also identified one patient who was found to have undiagnosed Fabry disease and one patient with McArdle's disease. One patient later presented with Pompé's after an acute illness. Including the latent case, the frequency of cases at 0.12% was lower than the 2.5% found in studies of patients with raised CK from neurology clinics (p<0.001). CONCLUSIONS: Screening pathology databases for elevated CK may identify patients with inherited metabolic errors affecting muscle metabolism. However, the frequency of Pompé's disease identified from laboratory populations was less than that in patients referred for neurological investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".