Flaws in the peer-reviewing process : a critical look at a recent paper studying the role of CCN3 in renal cell carcinoma
Bibliographic record
Abstract
A critical look at a recently published manuscript reporting the role of CCN3 in the regulation of clear cell renal cell carcinoma (RCC) biology raises several scientific concerns, and reveals flaws in the reviewing process which appear to have resulted in the dissemination of conclusions that are not supported by proper experimental procedures. In the example presented here, the observed biological effects are attributed to a high molecular weight "CCN3" protein which is detected by a single commercial antibody that was not shown in the experimental conditions used by the authors to be a valid reagent capable of stringently detecting the "canonical" CCN3 protein. Experiments establishing that inhibiting the production of high molecular weight "CCN3" protein would reverse these biological effects were not performed. The case discussed here clearly demonstrates that unreliable data can go through peer reviewing and be published. As the data can end up being cited and used as a potential reference by new investigators in the field, we believe that such data can throw roadblocks across the scientific path of inquiry and mislead investigations. We therefore raise awareness for the need of a more stringent peer reviewing process in which assurance can be had that the strength and precision of the data have been thoroughly checked by experts in the CCN field, and previous work properly referenced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.174 | 0.477 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.022 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".