Validation, limitations, and reviewer responsibilities

Validation posture

Outputs should be interpreted as evidence-backed workflow artifacts: search candidates, screening suggestions, extracted values, appraisal drafts, model results, certainty drafts, and report drafts. Each stage is designed to be inspectable before it becomes part of the final review.

www.axelium.ai · Analysis

Automation and human review

• AI screening suggestions can be wrong or incomplete; reviewer decisions should control formal inclusion.
• Extracted values can be affected by table structure, reporting ambiguity, OCR quality, or conflicting sources.
• Risk-of-bias and certainty assessments require reviewer validation before publication.
• Generated reports are drafts and must be checked against the protocol and evidence.

Search, screening, and full text

Search coverage depends on source availability, indexing lag, query quality, and access rights. Optional source coverage and citation chasing improve recall but do not guarantee completeness. Full-text retrieval can be blocked by paywalls, publisher gates, or poor metadata; manual upload and the browser companion help resolve these cases when you have lawful access.

Extraction limitations

Complex tables, unusual outcome definitions, supplement-only data, multi-publication study bundles, and inconsistent registry versus publication values all require reviewer attention. Use source links, confidence indicators, and review queues to verify important values before pooling.

Statistical limitations

• Small numbers of studies make heterogeneity, asymmetry, and subgroup checks unstable.
• Model choice should be justified by the protocol and data, not selected after seeing the preferred result.
• Effect measures, timepoints, and direction of benefit must be harmonised before pooling.
• Exploratory analyses should be labelled as exploratory in reports.

Network meta-analysis

Axelium supports clinical network meta-analysis with reviewer-approved treatment definitions, readiness checks, and certainty review. Network setup warnings, multi-arm study caveats, sparse evidence, and inconsistency signals should prompt methodological review before results are reported.

Ecology and evolution meta-analysis

Ecology/evolution support is in controlled rollout and currently centres on multilevel synthesis for dependent effects, with an optional additive meta-regression on recorded moderators and narrative certainty. Results are reported on the effect measure's natural scale with its null anchor.

• Phylogenetic meta-analysis is not supported.
• Moderator meta-regression is additive only; interaction terms between moderators are not fitted.
• A total heterogeneity summary with prediction intervals is reported for multilevel models; a partitioned breakdown across variance components is not.
• When sampling covariances among dependent effects are not supplied, those effects are treated as independent and the pooled uncertainty may be understated; this assumption is surfaced on the result.
• Clinical GRADE, CINeMA, and ranking-style clinical interpretation are outside the current public scope.

Qualitative and mixed-methods synthesis

CASP appraisal, coding, theme development, and CERQual confidence ratings are reviewer-owned. Automated drafts can accelerate the groundwork, but analytical findings and confidence rationales must be reviewed, edited, and finalised by the review team.

Review automation

Automated review may flag missing data, stale appraisals, unit issues, or protocol-level suggestions. Protocol-level changes should pause for explicit approval. Applied changes should be disclosed when they affect the reported review.

Access, teams, and billing

Team roles, billing status, and connected-app authorization affect who can start work, review evidence, approve changes, and capture full text. Keep team membership and connected apps current for long-running reviews.