Reliability of studies published as SR/MA on nutrition in cancer prevention - a systematic survey
Bibliographic record
Abstract
Abstract Background In several fields of medicine, the quality of studies published as systematic reviews/meta-analyses (SR/MAs) is low. Similar problems may exist for SR/MA on nutrition in cancer prevention. We aimed to assess overall quality and risk of bias (RoB) of studies published as SR/MA on nutritional interventions in cancer prevention with two instruments: AMSTAR 2 ('a measurement tool to assess systematic review 2') and ROBIS ('Risk of Bias in Systematic Reviews') respectively. Methods Following a systematic search in 3 databases we included studies identified as SR/MA published between 2010 and 2018 assessing any nutritional interventions in cancer prevention in the general population or among people with cancer risk (Protocol in PROSPERO CRD42019121116). All the steps of study selection and data extraction were done by two independent reviewers with conflicts solved by discussion or by the third reviewer. Results We focused on a subsample of 101 SR/MA randomly selected from 737 included SR/MA. Included SR/MA on average searched 2 databases with Medline in 98% and included cohort studies (93%). They focused on specific food (36%), specific nutrients (27%) or beverages (24%, mostly tea and coffee). The assessment using AMSTAR 2 tool indicated that 93% of SR/MA had no pre-specified methodology, in 77% - research questions and inclusion criteria did not include the components of PICO, RoB assessment of primary studies was not used or did not contain all elements (87%) and RoB was not accounted for in the interpretation of the results (75%). Overall, the quality of 97% of studies was assessed as critically low. In the ROBIS tool for 97% of included studies, the overall high risk of bias was detected. The most important methodological flaws in ROBIS were similar to identified in AMSTAR tool. Conclusions Poor quality of SR/MA due to flawed methodology may lead to many concerns and mislead public media and consumers. Key messages Poor quality of SR/MA due to flawed methodology may lead to many concerns and mislead public media and consumers. The studies published as SR/MAs addressing nutrition for cancer prevention have major flaws, which limit the reliability of their conclusions.
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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 | 0.455 | 0.796 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.034 | 0.032 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".