{"publication_id":"55002373-6c0b-43a9-8d3e-ecfdd6583aea","screening":{"identified":15,"screened":15,"excluded":0,"included":15,"included_or_retained":15,"flow":["identified","screened","excluded_with_reasons","included"],"wording":"15 candidate receipts retained after source retrieval, deduplication, and topic filtering. This is an evidence-map screening trace, not a PRISMA full-text exclusion audit.","exclusion_reasons":["No PRISMA full-text exclusion-stage filter was applied."]},"limitations":["This is an agent-assisted evidence map, not a PRISMA-complete systematic review or clinical guideline.","It is not PROSPERO-registered and should not be read as medical advice.","Public sidecars expose citation traces and extraction status; empty fields mean not extracted, not assumed absent."],"contradictions":["Evidence-honesty note: 11/15 retained sources are indirect, review-level, adjacent, or mechanistic and are used only to bound interpretation. The conclusion therefore does not support broad causal, clinical, or policy claims. Resistance training (RT) is widely prescribed to improve muscle mass, glycemic control, and frailty-related outcomes, yet the breadth of claimed benefits spans cardiometabolic, musculoskeletal, immune, and quality-of-life endpoints without a unified quantitative synthesis. Because individual trials enroll heterogeneous populations — type 2 diabetes (T2DM), multiple sclerosis, sarcopenic older adults, youth athletes, and hypothyroid patients — the central clinical question is whether RT produces consistent, clinically meaningful effects on cardiometabolic and muscle-function endpoints across these adult populations, and where the boundary conditions lie. We conducted an AI-assisted structured evidence synthesis of 15 curated reference papers, linking each source to study design, outcome class, and directness to maintain an audit trail from primary findings to the integrating claim.","The corpus contains 4 direct clinical sources, 11 adjacent, review, or context sources, and no sources classified primarily as mechanistic or model-system evidence. That distribution makes the synthesis appropriate for evaluating convergence, boundary conditions, and trial-design implications, while requiring caution around any conclusion that would exceed the direct human evidence.","Null findings have a specific role in this evidence model. They do not erase mechanistic plausibility, but they do narrow the set of claims that can be made about effect consistency, target population, and endpoint selection.","The evidence base also distinguishes breadth from certainty. A broad corpus can cover many biological domains while still leaving the clinically decisive question unresolved if direct evidence is limited, heterogeneous, or endpoint-specific.","The direct evidence establishes what has been observed in human or adjacent clinical settings. The mechanistic evidence helps explain why an effect might be plausible, but it does not by itself establish the size, durability, or safety of a human healthspan effect.","The study-level structure also prevents selective emphasis. Supportive, null, mixed, and adverse findings remain visible in the same manuscript, allowing the reader to distinguish evidential breadth from evidential certainty.","| Contextual Adjacent Evidence | Tan 2026: Effects of different training on lower limb explosive power in youth soccer players: a systematic review and network meta-analysis | direction=mixed | directness=review | B2 | outcome=Contextual Adjacent Evidence; direction=mixed | finding=representative non-significant statistic P = 0.098; not treated as positive or negative directional support unless source direction is coded |"]}