An assessment of inter-rater agreement of the literature filtering process in the development of evidence-based dietary guidelines
Bibliographic record
Abstract
OBJECTIVE: To determine whether the literature filtering process, a vital initial component of a systematic literature review, could be successfully completed by nutrition professionals or non-professionals. DESIGN: Using a diet-disease relationship as the guideline topic, inter-rater agreement for the title and abstract filtering processes between and among professionals and non-professionals was assessed and compared with an expert reference standard. Predetermined eligibility criteria were applied by all raters to 185 titles and 90 abstracts. Filtering decisions were initially made independently and then revised after a within-pair consensus meeting. SUBJECTS: The raters were six dietitians (RD) and six nutrition graduate students (Grad). To assess inter-rater agreement (reliability), each group was divided into three pairs. RESULTS: Weighted and unweighted kappa statistics and percentage agreement were calculated to determine the inter-rater agreement within pairs. Sensitivity and specificity estimates were determined by comparing responses with those of an expert reference standard. Overall, Grad pairs demonstrated greater inter-rater agreement than RD pairs for title filtering (P<0.05); no differences were observed for abstract filtering. Compared with the expert reference standard, every rater and pair had false-negative responses for both title and abstract filtering. CONCLUSIONS: After consensus meetings, both RDs and Grads were comparable in their agreement on title and abstract filtering, although important differences remained compared with the expert reference standard. This study provides preliminary findings on the value of utilising a non-expert pair in developing guidelines, and suggests that the literature filtering process is complex and quite subjective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.543 | 0.692 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".