Protecting intellectual property associated with health technology trials – another barrier to multi-centre trials?
Bibliographic record
Abstract
To examine the approaches to protection of intellectual property in multi-centre trials currently being conducted in Canada. Two ongoing international multicentre perinatal trials, both funded by the Canadian Institutes of Health Research, were selected for study on the basis of their contrasting approaches to protecting intellectual property. These approaches were examined in detail to understand their motivation, and to estimate the impact of these approaches on centre recruitment. CHIPS (Control of Hypertension in Pregnancy Study, ISRCTN71416914) – is recruiting 1028 pregnant women in 14 countries. Women with hypertension are randomised to tight or less tight control of hypertension. Primary outcome: composite of pregnancy loss/neonatal intensive care. Intellectual property is safeguarded by publishing the protocol online [ 1 ]. Positive consequences: possible/actual sites have easy access to full study design; potential for open discussion between collaborators; study investigators will be held to high standards of reporting. Negative consequences: details are available with potential for plagiarism. FACT (Folic Acid Clinical Trial, ISRCTN23781770) – is recruiting 3656 pregnant women in 4 countries. Pregnant women are randomised to receive either 4 mg folic acid or placebo daily. Primary outcome: development of pre-eclampsia. Intellectual property is safeguarded by requiring local investigators/institutions to sign non-disclosure agreements (NDAs) before the full protocol is provided. Positive consequences: details of the study not available unless legal agreement is signed. Negative consequences: may restrict academic openness; provide additional barriers to site recruitment; investigators may present selected results. The two trials illustrate contrasting approaches to protecting intellectual property associated with study design. This issue is becoming more important for academic institutions whose reputations and wealth are influenced by ownership and management of the intellectual property generated by faculty members. Some institutions prefer to manage risk using legal measures. In the case of trials, institutions protect their intellectual property by introducing NDAs into the sub-site agreement process. NDAs between the lead institution and sub-sites may represent a legally responsible approach. Unfortunately there are potential disadvantages: adding an extra legal step into sub-site recruitment will make this process more difficult; this step may reduce academic openness and collegiality; and restricting the availability of the protocol could allow investigators to present selected results. The use of non-disclosure agreements is an increasing trend in Canada. This trend will impact on the work of clinical trialists, perhaps making site recruitment even more difficult.
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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 | 0.840 | 0.900 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.028 | 0.040 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.022 | 0.029 |
| Insufficient payload (model declined to judge) | 0.024 | 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; 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".