Next-generation sequencing of 32 genes associated with hereditary aortopathies and related disorders of connective tissue in a cohort of 199 patients.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- 🟡Could not use the authors’ exact input data
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Renner et al. 2019 (Genet Med) is a clinical diagnostic NGS-panel study; the authors deposited NO raw data/code, but their PV/LPV interpretation results were deposited in ClinVar (UKE, OrgID 505251). The paper IS described well enough and the deposited pipeline output reproduces 1:1: 15 Pathogenic + 20 Likely-pathogenic variants, FBN1=23 and SMAD3=5, matching the paper exactly; diagnostic-yield arithmetic (7.5% / 9.6% / 17.1%) re-derives exactly. An independent third-party ACMG classifier (genebe) re-classified the 35 deposited variants and confirmed all 15 Pathogenic calls and 31/33 evaluable variants as P/LP (94%) -- the 2 LP->VUS downgrades and the LP->P upgrades are the expected automated-vs-manual ACMG gap. NOT attempted / not reproducible: the read->variant CALLING step (no public FASTQ/BAM/VCF; the 199-patient German clinical cohort has no EGA/dbGaP/SRA accession, controlled by privacy law), the 72 VUS (only PV/LPV were deposited), and HGMD-overlap/novelty/reclassification statistics (licensed HGMD). Verdict: PARTIAL (strong) -- everything public and pipeline-derived reproduces exactly; the unreproducible parts are unreproducible because the data was never made public, not because of a discrepancy.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 93assessed: 2026-06-18 ⛓ ee73e2a56816
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-18
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-18no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
All publicly checkable claims reproduce 1:1 from the UKE ClinVar deposit (15 Pathogenic + 20 Likely-pathogenic = 35; FBN1 23; SMAD3 5; yields 7.5%/9.6%/17.1% re-derive exactly, only a rounding-level 9.55→9.6% gap). No fabrication signal — values are derivable from shared data and an independent genebe ACMG run corroborates 94% of evaluable variants. The limitations are on the data-availability side, not the authors' or our method: the privacy-controlled 199-patient cohort has no public raw reads, so the variant-calling pipeline, the 72 VUS, and HGMD-novelty claims are unverifiable. One fair caveat: count matches compare the authors' own deposit to their paper, so the generative pipeline remains formally unverified.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.