SIGnature Libraries: A roadmap for the formation of special interest group libraries
Bibliographic record
Abstract
Objective: "SIGnature Libraries" channel the dynamism of academic society-based special interest groups (SIG) to systematically identify and provide user-oriented access to essential literature for a subspecialty field in a manner that keeps pace with the field's continuing evolution. The libraries include literature beyond clinical trial data to encompass historical context, diagnostic conceptualization, and community organization materials to foster a holistic understanding of how neurologic conditions affect individuals, their community, and their lived experience. Methods: = 75) administered two rounds of literature submissions and ratings. A final review by an 11-member international advisory group determined threshold ratings for resource inclusion and the library's final structure. Results: Seventy-nine articles were submitted for the first Delphi round and 22 articles for the second Delphi round. Survey response rates among SIG members were 29/75 for the first round and 24/75 for the second round. The advisory board added additional articles in the final review process in view of the overall project goal. A total of 60 articles were included in the final library, and articles were divided into seven sections and stratified by rating scores. A process for ongoing revisions of the library was determined. The library will be published on the Child Neurology Society website and made publicly accessible. Conclusions: The CP SIGnature Library offers learners an unprecedented resource that provides equitable access to latest consensus guidelines, existing seminal datasets, up-to-date review articles, and other patient care tools. A distinctive feature of the library is its intentional large scope and depth, presented in a stratified fashion relative to the consensus-determined importance of each article. Learners can efficiently navigate the library based on individual interests and goals, and the library can be used as core curriculum for CP education.
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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 | 0.204 | 0.204 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.050 | 0.046 |
| Open science | 0.012 | 0.056 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.093 | 0.053 |
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".