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Record W179367881

The Southern Alberta Renal Program database: a prototype for patient management and research initiatives.

2001· article· en· W179367881 on OpenAlexaffabout
Braden Manns, Garth Mortis, K. Taub, Kevin McLaughlin, Cam Donaldson, William A. Ghali

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDatabaseMedicineComorbidityQuality assuranceHealth careComputer scienceInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.326
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations107
Published2001
Admission routes2
Has abstractyes

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