Systematic review and REMARK scoring of renal cell carcinoma (RCC) prognostic circulating biomarker manuscripts.
Bibliographic record
Abstract
569 Background: No validated biomarkers exist to help guide prognosis of RCC patients. This study seeks to determine the current state of published prognostic RCC biomarker manuscripts and evaluate their quality using the REMARK criteria. Methods: The phrase “(renal cell carcinoma OR renal cancer OR kidney cancer OR kidney carcinoma) AND circulating AND (biomarkers OR cell free DNA OR tumor DNA OR methylated cell free DNA OR methylated tumor DNA)” was searched in Embase, Medline and PubMed during March 2018. One author (MI) selected all relevant manuscripts from the search results, and two authors (MI and SP) independently scored all relevant manuscripts using the REMARK guidelines (maximum 20 points comprised of 20 items subdivided into 48 criteria). Results: The search identified 525 publications: 73 were valid, 436 were rejected, and 26 were uncertain of their relevance. Amongst the valid publications, 33 were manuscripts of primary research (remainder: 26 review papers, 14 abstracts): manuscripts evaluating ≥ 2 biomarkers (n = 8) and novel biomarkers not published elsewhere (n = 7) comprised the majority. The median REMARK score was 10.6 (range 6.4-14.2). All manuscripts stated their marker, study objectives and method of case selection. The lowest scoring criteria were lack of: description of time between storage of blood/serum and marker assay (n = 2); flow or study profile diagram (n = 2); blinding of the person making the marker assessment to clinical outcomes (n = 3); and pre-specified hypotheses (n = 3). In total, only 8 studies reported a hazard or odds ratio. Using Pearson’s correlation, there was no association with either year of publication (median 2014; range 2004-2018; r2 = 0.14; p = 0.44) or impact factor (median 5.168; range 1.2-26.303; r2 = 0.24; p = 0.17) with REMARK score. Conclusions: Despite several published manuscripts on RCC prognostic biomarkers, most poorly adhere to the REMARK guidelines; this may be the cause for the paucity of a validated RCC biomarker to help supplement or supplant current clinical prognostic criteria. Better designed studies and appropriate reporting of methods, results and interpretation are required to address this urgent unmet need.
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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 | 0.063 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.043 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".