Bibliographic record
Abstract
This issue of Pain Medicine presents an important article that begins to address the complex issue of prescription drug abuse in America. While the authors acknowledge that one of the unstated goals of the program was to provide systematic data that would refute media claims of an “epidemic” of extended-release oxycodone abuse, Dr. Cicero and his colleagues now report that their data suggest that not only is there a significant problem, but that problem appears as an upward trend. Quite correctly, they suggest that this needs to be carefully monitored. While the authors acknowledge a number of weaknesses in this study, there are at least two very important strengths. First, their data come in a timely fashion, utilizing modern communication techniques and electronic data handling. In this respect, we are beginning to see problems, in a geographically sensitive distribution that may allow for timely and targeted interventions. Second, the data gathering model uses several different sources of information to help identify complementary signals that could be used to locate early signs of abuse, achieving an important goal of the Henney Report, which is to develop a proactive risk management strategy that would better protect the public by obtaining “real-time” evidence of emerging problems instead of historical trends.
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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.043 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".