A.1 A national eDelphi process to establish Canadian quality indicators for the care of adults hospitalized for neurological problems
Bibliographic record
Abstract
Background: Our aim was to develop a National Quality Indicators Set for the Care of Adults Hospitalized for Neurological Problems, to serve as a foundation to build regional or national quality initiatives in Canadian neurology centres. Methods: We used a national eDelphi process to develop a suite of quality indicators and a parallel process of surveys and patient focus groups to identify patient priorities. Canadian content and methodology experts were invited to participate. To be included, >70% of participants had to rate items as critical and <15% had to rate it as not important. Two rounds of surveys and consensus meetings were used identify and rank indicators, followed by national consultation with members of the Canadian Neurological Society. Results: 38 neurologists and methodologists and 56 patients/caregivers participated in this project. An initial list of 91 possible quality indicators was narrowed to 40 indicators across multiple categories of neurological conditions. 21 patient priorities were identified. Conclusions: This quality indicators suite can be used regionally or nationally to drive improvement initiatives for inpatient neurology care. In addition, we identified multiple opportunities for further research where evidence was lacking or patient and provider priorities did not align.
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 | 0.127 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".