Decentralized elements within oncology trials: Labcorp experience.
Bibliographic record
Abstract
e13630 Background: The concept of digital health using Decentralized elements (DE) has rapidly gained acceptance due to the COVID-19 pandemic, moving clinical trials (CTs) to a more patient-centric model (PCM) with fewer site visits. The primary aims of DE are reducing patient burden, increasing enrolment and retention, maintaining quality of life, and decreasing overall trial cost. Methods: Between 2011 and 2022, Labcorp conducted 491 CTs which incorporated DE: telemedicine, electronic clinical outcome assessments (eCOA), electronic informed consent (eConsent) and mobile clinical services (MCS). All CTs were analyzed by therapeutic area, study phase (1-4) and country. Results: The COVID-19 pandemic increased the implementation of DE. However, digital health uptake between therapeutic area differed. Comparing Oncology with Neurology (higher increase in research), whilst eCOA was implemented most in Oncology, the Neurology MCS increase was over 50% since 2020. Overall, Big Pharma implemented ~1.5% more Decentralized elements, vs the biotech sector. The majority were phase 3, followed by phase 2, with MCS increasing its demand in phase 1. By country, USA, Spain, France, Germany and Canada dominated. The majority were hybrid CTs, with few completely virtual. Conclusions: Labcorp experience demonstrates that overall, the interest to implement DE in CTs continues to grow, promoting the transition to a PCM. Nevertheless, solutions like MCS still need to gain more acceptance in Oncology due to the added complexity and logistics and will require training of all stakeholders, as well as understanding the benefits and potential country-specific limitations. [Table: see text]
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 | 0.078 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".