Bibliographic record
Abstract
Benchmarking is ‘a structured, continuous, collaborative process in which comparisons for selected indicators are used to identify factors which when implemented will improve transfusion practices’. In the Transfusion Medicine literature, there are only a few published articles that meet the criteria for benchmarking: (1) using comparisons between institutions to identify practice variation; (2) using a communication and/or evaluation process to identify factors associated with best practices; (3) introduce best practice factors into one's own setting; and (4) re‐evaluate performance. Three models for benchmarking have been proposed: (1) a regional benchmarking programme that collects and links relevant data from existing electronic sources; (2) a sentinel site model where data from a limited number of sites are collected; and (3) an institutional‐initiated model where a site identifies indicators of interest and approach other institutions as comparators. Finland has the most well‐developed benchmarking model where hospital data are collected electronically from multiple sources and analysed centrally with web‐based reports available for participants. Areas of practice variation are explored in annual benchmarking workshops, interventions are identified and implemented, and the impact of the interventions are evaluated at a later date. A provincial model used in Canada will also be described showing the impact on red cell outdating when hospitals were challenged to meet evidence based targets. Limitations of benchmarking and future research will be discussed.
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 | 0.022 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.016 |
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".