Opioid Prescribing on an Internal Medicine Teaching Unit
Bibliographic record
Abstract
ObjectiveTo investigate the rationale and timing of opioid prescriptions for Internal Medicine inpatients in an academic center in Saskatoon, Canada.MethodsWe performed a cross-sectional study of Internal Medicine inpatients that were prescribed opioids in Saskatoon. We examined documentation of clinical rationale and timing of opioid initiation or first escalation.ResultsOf 57 patients, 34 (60%) were opioid naive prior to admission and 48 (84%) had opioid doses either initiated or escalated. Of these 48 patients, 27 (56%) occurred during on-call hours. Rationale for escalation was documented in 31 cases (65%), with reasons including terminal care (17%), musculoskeletal pain (15%), and skin and soft tissue infections (13%).ConclusionRationale for opioid use was frequently not documented. Initial decision to change opioid dose occurred equally between daytime and on-call hours. RÉSUMÉObjectifÉtudier la justification et le moment choisi pour prescrire des opioïdes chez les patients hospitalisés en médecine interne dans un centre universitaire de Saskatoon (Canada).MéthodologieNous avons mené une étude de prévalence sur des patients hospitalisés en médecine interne chez qui on a prescrit des opioïdes à Saskatoon. Nous avons examiné la documentation concernant la justification clinique et le moment choisi pour entreprendre le traitement par les opioïdes ou effectuer la première augmentation de dose. RésultatsDes 57 patients, 34 (60 %) n’avaient jamais pris d’opioïdes avant leur hospitalisation et 48 (84 %) ont reçu leur première dose d’opioïdes ou une augmentation de dose. De ces 48 patients, 27 (56 %) ont reçu leur dose durant les heures de garde. La justification de l’augmentation de dose est documentée dans 31 cas (65 %), les raisons étant les soins de fin de vie (17 %), la douleur musculosquelettique (15 %) et les infections de la peau et des tissus mous (13 %).ConclusionSouvent, la justification de l’utilisation des opioïdes n’est pas documentée. Le moment où la décision initiale de modifier la dose d’opioïde est prise est réparti de façon égale entre le jour et durant les heures de garde.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".