An evaluation of harvest plots to display results of meta-analyses in overviews of reviews: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Harvest plots are used to graphically display evidence from complex and diverse studies or results. Overviews of reviews bring together evidence from two or more systematic reviews. Our objective was to determine the feasibility of using harvest plots to depict complex results of overviews of reviews. METHODS: We conducted a survey of 279 members of Cochrane Child Health to determine their preferences for graphical display of data, and their understanding of data presented in the form of harvest plots. Preferences were rated on a scale of 0-100 (100 most preferred) and tabulated using descriptive statistics. Knowledge and accuracy were assessed by tabulating the number of correctly answered questions for harvest plots and traditional data summary tables; t-tests were used to compare responses between formats. RESULTS: 53 individuals from 7 countries completed the survey (19%): 60% were females; the majority had an MD (38%), PhD (47%), or equivalent. Respondents had published a median of 3 systematic reviews (inter-quartile range 1 to 8). There were few differences between harvest plots and tables in terms of being: well-suited to summarize and display results from meta-analysis (52 vs. 56); easy to understand (53 vs. 51); and, intuitive (49 vs. 44). Harvest plots were considered more aesthetically pleasing (56 vs. 44, p = 0.03). 40% felt the harvest plots could be used in conjunction with tables to display results from meta-analyses; additionally, 45% felt the harvest plots could be used with some improvement. There was no statistically significant difference in percentage of knowledge questions answered correctly for harvest plots compared with tables. When considering both types of data display, 21% of knowledge questions were answered incorrectly. CONCLUSIONS: Neither harvest plots nor standard summary tables were ranked highly in terms of being easy to understand or intuitive, reflecting that neither format is ideal to summarize the results of meta-analyses in overviews of reviews. Responses to knowledge questions showed some misinterpretation of results of meta-analyses. Reviewers should ensure that messages are clearly articulated and summarized in the text to avoid misinterpretation.
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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 | 0.432 | 0.689 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".