Are Study and Journal Characteristics Reliable Indicators of “Truth” in Imaging Research?
Bibliographic record
Abstract
Purpose To evaluate whether journal-level variables (impact factor, cited half-life, and Standards for Reporting of Diagnostic Accuracy Studies [STARD] endorsement) and study-level variables (citation rate, timing of publication, and order of publication) are associated with the distance between primary study results and summary estimates from meta-analyses. Materials and Methods MEDLINE was searched for meta-analyses of imaging diagnostic accuracy studies, published from January 2005 to April 2016. Data on journal-level and primary-study variables were extracted for each meta-analysis. Primary studies were dichotomized by variable as first versus subsequent publication, publication before versus after STARD introduction, STARD endorsement, or by median split. The mean absolute deviation of primary study estimates from the corresponding summary estimates for sensitivity and specificity was compared between groups. Means and confidence intervals were obtained by using bootstrap resampling; P values were calculated by using a t test. Results Ninety-eight meta-analyses summarizing 1458 primary studies met the inclusion criteria. There was substantial variability, but no significant differences, in deviations from the summary estimate between paired groups (P > .0041 in all comparisons). The largest difference found was in mean deviation for sensitivity, which was observed for publication timing, where studies published first on a topic demonstrated a mean deviation that was 2.5 percentage points smaller than subsequently published studies (P = .005). For journal-level factors, the greatest difference found (1.8 percentage points; P = .088) was in mean deviation for sensitivity in journals with impact factors above the median compared with those below the median. Conclusion Journal- and study-level variables considered important when evaluating diagnostic accuracy information to guide clinical decisions are not systematically associated with distance from the truth; critical appraisal of individual articles is recommended. © RSNA, 2017 Online supplemental material is available for this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.256 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".