Knowledge Gaps in Reproductive Health among Women with MS (KNOWwMS): a project protocol
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic condition affecting primarily women in childbearing age. Studies have highlighted the significant challenges and limitations women face in accessing reliable and consistent information on reproductive health from neurologists and other healthcare professionals, especially in limited resources settings. In these global settings, there is a need to explore the core set of information about MS and its impact on their reproductive health that could lead to better awareness and self-reliance. The project aims to define a set of information that any woman with MS should know (“Core Knowledge Set”), tailored towards resource-limited settings. This set will be aimed at filling key knowledge gaps reported by women with MS regarding the connection between MS and reproductive health. Additionally, it may serve as a valuable tool to support the adoption of evidence-based recommendations for improving access to MS-related information. A mixed methodology has been developed for informing the set through two stages: (a) mapping the available evidence by means of a systematic scoping review of the published literature addressing information needs among women with MS, and (b) two international, anonymous on-line surveys aimed at assessing access to information sources and knowledge gaps from the perspective of women as well as MS healthcare professionals, focusing on limited resources settings.
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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 | 0.085 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.009 |
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".