Bibliographic record
Abstract
The FDA is critical in ensuring that medications are safe and effective. However, the FDA's decision-making process for opioid analgesics is complicated by the need to address patients with complex clinical pain syndromes while balancing public safety concerns involving opioid misuse and abuse. Several recent regulatory decisions by FDA have exposed the complexity of this regulatory tug of war. For example, the FDA's decision to include a requirement for tamper resistance for extended-release oxycodone products but not for extended-release oxymorphone or hydrocodone preparations is concerning. Although tamper resistance is an imperfect solution, it provides a modicum of abuse prevention. Additionally, the rewording of the labeled indication (from 'moderate to severe pain' to 'severe enough pain') for extended-release opioid analgesics, in an attempt to provide clarity, resulted in an equally if not more vague statement of appropriate use. Furthermore, the postmarketing requirement for continued data regarding safety and efficacy have been affirmed by FDA but some of the proposed means to acquire those data will likely result in unclear answers and may have undesired consequences. We fully support the important role of the FDA but raise concerns about the occasional lack of consistency and transparency.
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 | 0.022 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.033 | 0.040 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".