BGPT: Author Review: R Kogo

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Quick Explanation Copied

R Kogo scientific signal (from provided data): strong bibliometric footprint in cancer/oncology literature (OpenAlex top-match “Ryunosuke Kogo” shows h-index 17; cited_by_count 2901; works_count 86). Most directly verifiable publications in the dataset include high-impact venue work such as Nature Medicine () and Cancer Research ().

Key limitation: the provided “R Kogo” could be multiple distinct people; the dataset includes several similarly named authors. I therefore assess strength primarily for the top OpenAlex match provided, not for every “R Kogo” string variant.

Long Explanation

Author Review: R Kogo

Date context: April 09, 2026. Evidence scope: only what appears in your provided dataset (OpenAlex-like metrics, example DOIs, and one additional plant paper dataset). No uncited claims are added.

Identity check (critical):

Your provided data includes multiple potential matches for the query string “R Kogo” on OpenAlex (e.g., “Ryunosuke Kogo” with h-index 17; and “Reiri Kogo” with h-index 2, cited_by_count 67, works_count 4). This introduces name-collision risk: bibliometrics and publication lists may refer to different individuals.

1) Bibliometric footprint (from provided OpenAlex top-match data)

The strongest matched identity in your dataset is Ryunosuke Kogo (OpenAlex id shown in your data). Metrics provided: works_count = 86, cited_by_count = 2901, h-index = 17, and yearly counts also provided.

Interpretation (skeptical): citation metrics can be skewed by field size, review/author position, venue effects, and older work accumulating citations (i.e., not a direct measure of rigor per paper).

Notable anomaly from your provided data: a very large cited_by_count in 2011 (1626) alongside only 4 works. That pattern is plausible for a single highly cited paper, but it also can reflect how “counts_by_year” are mapped in your dataset (e.g., year of citation accumulation vs. year-of-publication). Treat this as data-structure dependent rather than direct biological or scientific inference.

2) Example high-impact publications explicitly present in your dataset

I list DOIs only as identity-linked evidence that an oncology-focused author profile exists in your provided data. I do not infer study outcomes, sample sizes, or conclusions beyond what is explicitly provided.

DOI	Title (as provided)	Year / Venue (as provided)	Authorship note (as provided)
10.1158/0008-5472.can-11-1021	Long Noncoding RNA HOTAIR Regulates Polycomb-Dependent Chromatin Modification and Is Associated with Poor Prognosis in Colorectal Cancers	2011, Cancer Research	Ryunosuke Kogo listed as first author in your OpenAlex excerpt
10.1038/nm.2392	Regulation of the MDM2-P53 pathway and tumor growth by PICT1 via nucleolar RPL11	2011, Nature Medicine	Ryunosuke Kogo appears among coauthors in your excerpt
10.3892/or.2012.2219	Clinical significance of the expression of long non-coding RNA HOTAIR in primary hepatocellular carcinoma
10.1158/1078-0432.ccr-10-2866	Clinical Significance of miR-146a in Gastric Cancer Cases	2011, Clinical Cancer Research	Ryunosuke Kogo listed as first author in your excerpt

Evidence objects above are supported by the DOI links below:

3) Critical methodological lens (what we can and cannot conclude from the provided dataset)

What is strong: your dataset provides bibliometric indicators (h-index, citations, works_count) and explicit DOIs for selected publications, enabling reproducible lookup.

What is weak / missing: there is no full-text content for the listed cancer/oncology papers in your prompt, so I cannot evaluate experimental design quality (controls, blinding, replication, effect sizes), nor can I assess reproducibility or publication bias directly.

Name-collision risk: “R Kogo” can refer to multiple authors. Without ORCID-level identity disambiguation for every included record, bibliometrics may be conflated.

Counterpoint: citation counts reflect community attention and can be influenced by trendiness and venue. They do not automatically measure mechanistic correctness.

4) Additional raw-data item you provided (plant polyphenols under salt stress)

Your message includes a separate dataset describing a plant study about polyphenolics in Arabidopsis thaliana cat2 knockout under salt stress. This item is not linked to “R Kogo” identity in what you provided, so I treat it as an unresolved evidence fragment.

The plant study is anchored by DOI 10.7124/visnyk.utgis.14.2.683 in your dataset. It states the Folin–Ciocalteu assay non-specificity limitation and the extracted design/timepoints.

5) Overall scientific strength assessment (evidence-limited)

Bibliometric strength: for the top OpenAlex match in your dataset, metrics are consistent with an established oncology research contributor (h-index 17; 2901 citations; 86 works). (Metric values come from your provided data; not re-derived.)
Scientific uncertainty: because you did not provide full-text experimental details for the oncology publications, I cannot judge experimental rigor, statistical robustness, or mechanistic validity directly.
Primary blindspot: name ambiguity (multiple “R Kogo” matches) and missing full-text evidence prevent a confident, paper-by-paper quality audit.

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Updated: April 09, 2026