Bibliographic record
Abstract
Abstract Clinical research using human participants to further medical knowledge has been at the forefront in 2021. Clinical research studying the efficacy of treatments can be categorised in two broad categories as ‘observational studies’ or ‘clinical trials’. Written from the perspective of a localization project manager at Vitaccess, which conducts global digital research for biopharmaceutical companies, this paper discusses five core challenges that impact the localization of such a study launched in France, Italy, Germany, Belgium, Spain, Japan, the UK, the US and Canada, conducted via a smartphone app. The localization project manager role provides a bridge between translators, revisers, ethics bodies, authors, legal, and medical reviewers, enabling oversight to keep the balance between launching the study globally and enabling each country to have the content and structure tailored to their cultural and linguistic expectations through localization. The main challenges in localizing a real-world evidence study is the complexity and volume of ethical, legal, and medical feedback required for the content of the study, which is further complicated by the need to target different countries and languages. Subjectivity and variance in the feedback per country also pose difficulties. International harmonisation of ethical, medical, and legal reviews of such global studies could streamline the process.
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 | 0.266 | 0.487 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.065 | 0.022 |
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".