The Urgent Need for Clinical Research Reform to Permit Faster, Less Expensive Access to New Therapies for Lethal Diseases
Bibliographic record
Abstract
High costs of complying with drug development regulations slow progress and contribute to high drug prices and, hence, mounting health care costs. If it is exorbitantly expensive to bring new therapies to approval, fewer agents can be developed with available resources, impeding the emergence of urgently needed treatments and escalating prices by limiting competition. Excessive regulation produces numerous speed bumps on the road to drug authorization. Although an explosion of knowledge could fuel rapid advances, progress has been slowed worldwide by inefficient regulatory and clinical research systems that limit access to therapies that prolong life and relieve suffering. We must replace current compliance-centered regulation (appropriate for nonlethal diseases like acne) with "progress-centered regulation" in lethal diseases, where the overarching objective must be rapid, inexpensive development of effective new therapies. We need to (i) reduce expensive, time-consuming preclinical toxicology and pharmacology assessments, which add little value; (ii) revamp the clinical trial approval process to make it fast and efficient; (iii) permit immediate multiple-site trial activation when an eligible patient is identified ("just-in-time" activation); (iv) reduce the requirement for excessive, low-value documentation; (v) replace this excessive documentation with sensible postmarketing surveillance; (vi) develop pragmatic investigator accreditation; (vii) where it is to the benefit of the patient, permit investigators latitude in deviating from protocols, without requiring approved amendments; (viii) confirm the value of predictive biomarkers before requiring the high costs of IDE/CLIA compliance; and (ix) approve agents based on high phase I-II response rates in defined subpopulations, rather than mandating expensive, time-consuming phase III trials.
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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 | 0.179 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.026 | 0.043 |
| Insufficient payload (model declined to judge) | 0.030 | 0.019 |
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".