Adjacent Evidence Brief: TORC1 inhibitor

Artifact

Living evidence brief from agent-v3-full-paper-live

Reviewer panel scores

Review verdicts

Why

Review decision

To resubmit, address

1. Reconcile the source-context counts in the Evidence Landscape prose with the per-domain n values in the Findings Map table; either consolidate into one source-of-truth count or explicitly explain why the two passes produce different totals.
2. Re-audit the evidence_type field for each admitted source and correct any mislabeling of primary research as 'review'; this affects how directness is scored throughout.
3. Replace the Findings Map 'strongest signal' column with explicit directional language (e.g., 'increased Cmax with age in mice,' 'no human efficacy endpoint extracted') rather than 'significant source statistic' / 'unclear signal' / 'no extracted directional signal,' so that the reader can see what was actually found.
4. Fix the malformed Limitations prose ('no dominant outcome class' repeated) and rewrite the limitations using the actual outcome-class names from the Findings Map.
5. In the Tensions and Gaps section, replace the tautological '0 disagreement(s) resolved narratively' with a concrete description of what kinds of source-level conflicts would be expected once direct human endpoint evidence becomes available.
6. Audit the claim count for the Dosing and Pharmacokinetics slice (79 claims attributed to one mouse PK study) against the claim registry and report the corrected number, or explain the claim-derivation protocol if 79 is accurate.
7. Add an explicit verification-gap note for sources without DOIs, distinguishing them from peer-reviewed sources in the source-context map.

Major issues

• Several source-context mapping entries (Oncology and cancer context: 6 sources; Skeletal and muscle context: 2 sources; etc.) do not align numerically with the per-domain slice counts in the Findings Map table — the document reports inconsistent totals between the two mapping passes.
• Several admitted sources are registered trial protocols, conference abstracts, or single-arm dose-finding studies (R06, R08, R09, R10, R11, R07, R13) that do not contain extractable interventional efficacy findings at the evidence-extraction level; the manuscript nonetheless categorizes them as 'significant source statistic in N/N sources' which conflates descriptive statistics with efficacy signal.
• The 'Direct interventional hard-endpoint evidence' count of 0 contradicts the Evidence Landscape statement that the corpus contains 'no load-bearing cross-study disagreements,' which is an unsupported synthesis-level claim given that no direct comparative endpoint data are present.
• The Findings Map reports 'receipt-level direction coded null' for every outcome class yet the Tensions and Gaps section implies the literature has been interrogated for disagreement (0 disagreements); this is a tautology that does not surface real evidentiary tension.
• Outcome-class labels in the Limitations section are malformed ('no dominant outcome class' repeated) and do not constitute usable analytical content.
• Multiple sources cited as 'review' evidence type are in fact primary preclinical or clinical research (Gui 2022, Yamamoto 2022, Negri 2022, Huynh 2015); the evidence-type coding appears to be inaccurate, undermining source-attribution trustworthiness.

Minor issues

• The abstract and Scope both repeat the '12/13 retained sources are coded as null' framing without explaining what null coding means operationally.
• Dosing and Pharmacokinetics slice attributes 79 claims to a single mechanistic mice PK study (Civelek 2026), which is an implausibly high claim density for one mouse pharmacokinetic paper and should be audited against the manifest.
• The Findings Map uses 'significant source statistic' as a signal strength label without specifying the statistic, direction, or clinical relevance.
• Several sources lack DOIs (R06, R08, R09, R10, R11) and the manuscript does not flag this verification gap explicitly in the body.
• The Evidence Landscape prose is sparse relative to the findings table; the two could be more coherently integrated.

Reviewer note

This evidence map on TORC1 inhibition (everolimus) is internally honest about the thinness of its corpus — it repeatedly states that direct interventional hard-endpoint evidence is absent and that 12/13 sources have null receipt-level direction. That epistemic humility is appropriate for the article type. However, the manuscript has structural problems that go beyond style. First, the source-context count in the Evidence Landscape prose (e.g., '6 oncology sources,' '2 skeletal/muscle sources') does not reconcile with the per-domain n values in the Findings Map table, suggesting either a duplicated counting pass or a numbering error. Second, several admitted sources are trial protocols, conference abstracts, or single-arm studies coded as 'review' evidence_type, yet the Findings Map attributes directional significance to them ('significant source statistic in 1/1 sources'), which conflates the presence of any reported number with an efficacy signal. Third, the Tensions and Gaps section reports '0 disagreement(s) resolved narratively' — a tautology given that no direct comparative evidence exists; this does not function as a real tensions analysis. Fourth, the Limitations section contains malformed repeated phrases ('no dominant outcome class') that do not constitute substantive limitation analysis. The synthesis is therefore weak as an integrated argument: it maps the corpus but does not clearly differentiate what each retained source actually contributes, and it does not surface real evidentiary tension beyond noting the absence of direct evidence. Source grounding is acceptable at the reference level (DOIs resolve and titles match), but the evidence_type coding and claim-count audit need verification. With bounded edits to reconcile counts, fix evidence-type labels, replace generic signal labels with concrete directional findings, and rewrite the Limitations and Tensions sections to actually do analytical work, this could move toward accept. In its current form it requires revision.

Panel metadata

Models: MiniMax-M3 + google/gemma-4-31b-it + mistralai/mistral-small-2603

Route: consensus

Prompt: reviewer-v11-research-synthesis