Screening for patients with Gaucher’s disease using routine pathology results: PATHFINDER (ferritin, alkaline phosphatase, platelets) study
Bibliographic record
Abstract
Aims: Lysosomal β-glucocerebrosidase (GBA) deficiency causes Gaucher disease (GD), a recessive disorder caused by bi-allelic mutations in GBA. The prevalence of GD is associated with ethnicity, but largely unknown and potentially underestimated in many countries. GD may manifest with organomegaly, bone involvement and neurological symptoms as well as abnormal laboratory biomarkers. This study attempted to screen for GD in patients using abnormal platelet, alkaline phosphatase (ALP) and ferritin results from laboratory databases. Methods: Electronic laboratory databases were interrogated using a 2-4 year time interval to identify from clinical biochemistry records patients with a phenotype of reduced platelets (<150x109/L) and either elevated ALP (>130iu/L) or ferritin (>150 (female) or >250µg/L(male)). The mean value over the screening window was used to reduce variability in results. A dried blood spot sample was collected for the determination of GBA activity in patients meeting these criteria. If low GBA activity was found then the concentration of the GD-specific biomarker glucosyl-sphingosine (lyso-GB1) was assayed, and the GBA gene sequenced. Results: Samples were obtained from 1058 patients; 232 patients had low GBA activity triggering further analysis. No new cases of GD with homozygosity for pathogenic variants were identified but 12 patients (1%) were identified to be carriers of a pathogenic variant in GBA. Conclusions: Pathology databases hold routine information that can be used to screen for patients with inherited errors of metabolism. However, biochemical screening using mean platelets, ALP and ferritin has a low yield for unidentified cases of Gaucher Disease.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".