The efficacy of coenzyme <scp> Q <sub>10</sub> </scp> treatment in alleviating the symptoms of primary coenzyme <scp> Q <sub>10</sub> </scp> deficiency: A systematic review
Bibliographic record
Abstract
Abstract Coenzyme Q 10 (CoQ 10 ) is necessary for mitochondrial electron transport. Mutations in CoQ 10 biosynthetic genes cause primary CoQ 10 deficiency (PCoQD) and manifest as mitochondrial disorders. It is often stated that PCoQD patients can be treated by oral CoQ 10 supplementation. To test this, we compiled all studies describing PCoQD patients up to May 2022. We excluded studies with no data on CoQ 10 treatment, or with insufficient description of effectiveness. Out of 303 PCoQD patients identified, we retained 89 cases, of which 24 reported improvements after CoQ 10 treatment (27.0%). In five cases, the patient's condition was reported to deteriorate after halting of CoQ 10 treatment. 12 cases reported improvement in the severity of ataxia and 5 cases in the severity of proteinuria. Only a subjective description of improvement was reported for 4 patients described as responding. All reported responses were partial improvements of only some symptoms. For PCoQD patients, CoQ 10 supplementation is replacement therapy. Yet, there is only very weak evidence for the efficacy of the treatment. Our findings, thus, suggest a need for caution when seeking to justify the widespread use of CoQ 10 for the treatment of any disease or as dietary supplement.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".