Implications of thermogenic adipose tissues for metabolic health
Part of the results reproduced; minor but material deviations remained.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Any deviation was negligible
- 🔴Could not use the authors’ exact input data
- 🔴Reported values were only indirectly comparable
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
▸Reproduction agent’s raw note
DROP (non_pipeline). The paper is a clinical-endocrinology review summarizing prior literature on thermogenic (brown/beige) adipose tissue and lipid/lipoprotein metabolism. It is NOT described well enough to reproduce because there is nothing computational to reproduce: a review generates no primary data, runs no bioinformatic pipeline, ships no code, and reports no pipeline-derived value. Not 1:1 and not 'different' -- there is simply no in-scope artifact. Nothing was attempted on «our HPC»/«infra»; doing so would be inappropriate. Honest drop, not a feasibility failure on our side.
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 assessmentassessed: 2026-06-18 ⛓ c25d8a46d81d
✎ 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.
Scope — PMID 27697210
Publication: Schlein C, Heeren J. Implications of thermogenic adipose tissues for metabolic health. Best Pract Res Clin Endocrinol Metab. 2016 Aug;30(4):487–496. DOI: 10.1016/j.beem.2016.08.002 · PMID: 27697210
Article type (decisive)
PubMed metadata pubtype = ["Journal Article", "Review"].
Abstract verbatim: "This review summarizes the current knowledge how thermogenic
tissues can be targeted to combat the burden of diseases with a special focus on
lipid metabolism and diseases related to lipoprotein metabolism."
This is a narrative review in a clinical-endocrinology review series (Best Practice & Research Clinical Endocrinology & Metabolism). It synthesizes prior literature on brown/beige adipose tissue, thermogenesis, and lipid/ lipoprotein metabolism. It is not an original-research article.
In-scope pipeline-derived results
None. A narrative review:
- generates no primary data (no GEO/SRA/ENA/ArrayExpress/figshare/zenodo/ dbGaP/EGA/PRIDE accession is reported or deposited);
- runs no bioinformatic/computational pipeline of its own;
- ships no analysis code (no repository named anywhere);
- reports no pipeline-derived numeric result/figure/table that could be recomputed from shipped data + code.
Any figures present are conceptual schematics summarizing other groups' findings, not outputs of a reproducible computation on a deposited dataset.
Out-of-scope (not attempted)
The entire article — it is review/synthesis content, explicitly out of scope per
BRIEF rule 2 ("wet-lab/manual/external") and SCREENING drop class non_pipeline.
Verdict
DROP — non_pipeline. No computational pipeline, no dataset, no pinnable
expected result to reproduce. Honest drop; no «our HPC» compute spent.
No individual results have been recorded for this entry yet.
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.
PMID 27697210 (Schlein & Heeren, Best Pract Res Clin Endocrinol Metab 2016) is a narrative review article (PubMed pubtype 'Review'), not an original study — it produces no data, runs no pipeline, ships no code, and reports no recomputable value, so it was correctly dropped as non_pipeline (a text-mining false positive). There is simply no in-scope artifact: q1/q2 red on data/endpoint availability, and no fabrication signal. Overall yellow reflects that there is nothing computationally reproducible, not any defect.
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.
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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.