The Southern Alberta Renal Program database: a prototype for patient management and research initiatives.
Bibliographic record
Abstract
The Southern Alberta Renal Program (SARP) database was developed to respond to an urgent need for local information on clinical outcomes, laboratory information, and health care costs, and to enable our local renal program to monitor the implementation of established clinical practice guidelines. The database captures detailed demographic, clinical, and laboratory information and is unique by also capturing comorbidity, health-related quality of life and costing information for patients with end-stage renal disease (ESRD) in southern Alberta, storing the information in one common database. By collecting information on patient comorbidity, health outcomes and costs, the SARP database has enabled many quality assurance initiatives as well as research opportunities for projects involving patients with ESRD. Due to the availability of links with other available local clinical and administrative databases, information is collected with a minimal need for manual data entry. This type of database is a method by which health programs could improve the quality of patient care. Programs caring for patients with chronic medical conditions such as ESRD should examine how computer databases could assist in clinical care and improve the efficiency with which that care is delivered to their patients.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 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".