Bibliographic record
Abstract
Quality improvement (QI) practices were originally developed in the manufacturing industry to reduce unnecessary steps in a process, minimize error, and provide maximum benefit to the consumer. QI is defined as a formal approach to the analysis of performance and systemic efforts to improve it. QI methodologies have been adopted by industries outside manufacturing, including healthcare. In the healthcare environment, performance consists of many factors including patient safety, clinical results, and system efficiency. Given the publicly funded, limited resource environment in which the Canadian healthcare system operates, the practice of delivering safe, quality healthcare in an efficient and cost-effective manner is an important factor in promoting the economic viability and sustainability of the system. Surgical practice has been identified as an area in which QI methodologies can be applied, given its resource intensive nature and highly regulated environment. Current research supports the use of QI in surgery, with interventions showing improvements in non-operativetime, on-time starts, and operating room patient volume. Limitations to the application of QI include the heterogeneity of interventions and variability in terms of procedures and patient factors. Further high-quality studies are required to support evidence-based applications of QI in the surgical setting as well as the greater healthcare environment.
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 | 0.100 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".