Index of application status transparency and availability of public information for Project Orbis agencies
Bibliographic record
Abstract
The purpose of this paper is to provide a guideline for understanding and comparing the regulatory framework of Project Orbis Partners (POPs) and Project Orbis observers, with a focus on their approaches to drug approvals, rejections, and withdrawals. While each agency has its own regulatory framework and guidance, there are some similarities and differences between the language used to describe drug approvals, rejections, withdrawals, and the public availability of these decisions. Project Orbis is an international partnership of regulatory agency, led by the U.S. Food and Drug Administration (FDA), aimed at streamlining the submission and review processes to expedite the global availability of oncology medications for patients. Since its inception, Australia’s Therapeutic Goods Administration (TGA), Brazil’s National Health Surveillance Agency (ANVISA), Canada’s Health Canada (HC), Israel’s Ministry of Health (IMoH) Medical Technologies, Health Information, Innovation and Research (MTIIR) Directorate, Singapore’s Health Sciences Authority (HSA), Switzerland’s Swissmedic (SMC), and United Kingdom’s Medicines and Healthcare Products Regulatory Agency (MHRA) have joined and become POPs. Other international agencies such as, the European Medicines Agency (EMA) and Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) are currently observing Project Orbis, as of late 2023. They are not full Project Orbis partners but work closely with the FDA to facilitate oncology drug approval through international collaboration. Running Title: An Index of the FDA and international regulators involved in Project Orbis looking at the similarities and differences between approval, rejection, and withdrawal characteristics of applications and public transparency of actions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".