Comorbidities and complications in adults with pyruvate kinase deficiency
Bibliographic record
Abstract
OBJECTIVES: Pyruvate kinase (PK) deficiency is caused by PKLR gene mutations, leading to defective red blood cell glycolysis and hemolytic anemia. Rates of comorbidities and complications by transfusion history and relative to the general population remain poorly quantified. METHODS: Data for patients aged ≥ 18 years with two confirmed PKLR mutations were obtained from the PK deficiency Natural History Study (NCT02053480). Frequencies of select conditions were compared with an age- and sex-matched cohort from a general insured US population without PK deficiency. RESULTS: Compared with the matched population (n = 1220), patients with PK deficiency (n = 122) had significantly higher lifetime rates of osteoporosis, liver cirrhosis, and pulmonary hypertension; splenectomy and cholecystectomy rates were also significantly higher in the 8 years before the index date. Sixty-five (53.3%) patients with PK deficiency were classified as regularly transfused, 30 (24.6%) as occasionally transfused, and 27 (22.1%) as never transfused. Regularly transfused patients were significantly more likely than never transfused patients to have had splenectomy, cholecystectomy, and/or thrombosis. Liver iron overload was reported in 62% of patients and occurred regardless of transfusion cohort. CONCLUSIONS: Even never transfused patients with PK deficiency had higher rates of select comorbidities and complications than individuals without PK deficiency.
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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 | 0.001 |
| 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".