Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the high rates of hospitalization among people who use drugs (PWUD), harm reduction interventions have not been widely adopted in inpatient settings. We list several harm reduction practices that we believe should be considered in hospitals. Interventions to decrease stigma, including guidance regarding language and partnering with people with lived experience of drug use, can be implemented expeditiously. Hospitals with a high prevalence of drug use can establish addiction consultation services to address issues including initiation of medication-assisted therapy. Prescription opioids as a treatment for opioid addiction for select patients require further implementation science research to determine how to adapt this intervention for inpatient settings. While the evidence base for needle and syringe programs in the community is strong, implementation science research is required to address how best to integrate such programs in hospitals. Such research is also required to determine the optimal programs to ensure continuity of care post-discharge and retention in addiction-related care. We believe that new evidence generation is required to address the optimal use of peripherally inserted central venous catheters, to determine the relative benefits and harms of treatment contracts for inpatients, and to assess the efficacy of supervised injection services for inpatients. The need for harm reduction programs in hospitals emphasizes the need for a pragmatic, patient-centered, non-judgmental approach to the care of PWUD.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it