Aggregate analysis of oxytocin incidents.
Bibliographic record
Abstract
Oxytocin is a valuable, time-tested drug and one of the most commonly used medications during labour and delivery.It acts on the smooth muscle of the uterus to stimulate contractions. In Canada, its uses include the induction of labour in patients with a medical indication for the initiation of labour; the stimulation and reinforcement of labour; and to control postpartum bleeding and hemorrhage.' As a high-alert medication, oxytocin bears a heightened risk of causing significant patient harm if used in error. For example, use of this drug to induce labour has been associated with significant adverse effects to both the mother (e.g., arrhythmias, uterine hyperstimulation, postpartum hemorrhage) and the fetus (e.g., bradycardia, hypoxia, hyperbilirubinemia, retinal hemorrhage).This bulletin shares information about incidents involving the use of oxytocin that have been voluntarily reported to the Canadian Medication Incident Reporting and Prevention System (CMIRPS). It includes an overview of the incidents and highlights major themes identified through a multi-incident analysis to raise awareness about continuous improvement opportunities for management of this medication.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".