Efficacy of CoQ10 supplementation for the treatment of primary CoQ10 deficiency
Bibliographic record
Abstract
Review question / Objective: How effective is oral CoQ10 supplementation to treat primary CoQ10 deficiency?Condition being studied: Coenzyme Q10 (CoQ10), also known as ubiquinone, is an essential component of the mitochondrial respiratory chain.CoQ10 is also known to be involved in several other cellular functions besides the respiratory chain.All cells rely exclusively on endogenous CoQ synthesis.So far, 13 COQ genes whose products participate in CoQ10 biosynthesis have been identified in humans.Mutations in COQ genes cause primary CoQ10 deficiency (PUD), a rare, clinically heterogenous, disorder.Symptoms are those of inborn mitochondrial respiratory chain disorders, including early onset, multi-organ involvement, and prevalence of neurological and muscular manifestations.With the increasing availability and affordability of genomic sequencing technology, more and more PUD patients and novel PUD disease variants are being reported.CoQ10 supplementation as replacement therapy is frequently initiated immediately after diagnosis, and the majority of the literature on CoQ10 deficiency claims that this treatment is effective.However, there is lack of clear evidence for this claim.The planned review aims to identify, summarize and evaluate all the available evidence for the effectiveness of CoQ10 supplementation for the treatment of PUD.INPLASY registration number: This protocol was registered with the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) on 25 February 2022 and was last updated on 25 February 2022 (registration number INPLASY202220113).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".