CHLA 2025 Conference Contributed Papers / ABSC Congrès 2025 Communications Libres
Bibliographic record
Abstract
Background: Citation searching is a valuable form of supplementary searching for scoping reviews, but is often time consuming.Several citation indexes and tools are now available to make this process more efficient, but it is unclear which may provide the best return on investment.Past studies have investigated the value of citation indexes in the context of systematic reviews or the coverage provided.However, these studies are limited in value as they do not include a robust grey literature search, do not include citation searching tools (e.g.CitationChaser), or do not quantify the value of the indexes with the number of relevant studies identified.Objective: To test citation searching indexes/tools in the context of a scoping review that includes grey literature to quantify value of each index/tool.Methods: A literature search was conducted to find citation searching indexes/tools that allow for bulk download of references.All citation indexes/tools were used for backward and forward citation searching in the scoping review.Recorded for each tool was: time needed to complete citation searching, total number of references retrieved and number of relevant references retrieved.Sensitivity and precision of backward and forward citation searching will be calculated for each tool using any relevant citations identified through citation searching as a reference set.Results: Descriptive statistics for each tool will be shared along with recommendations for which tool(s) may provide the best balance of time spent and relevant references found.
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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.742 | 0.632 |
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".