MétaCan
Menu
Back to cohort
Record W1911782486 · doi:10.24908/pceea.v0i0.3784

ANALYSIS OF SURGICAL PATIENT FLOW AT WINNIPEG HEALTH SCIENCES CENTRE

2011· article· en· W1911782486 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicOperations Management Techniques
Canadian institutionsWinnipeg Regional Health AuthorityUniversity of Manitoba
Fundersnot available
KeywordsQuality (philosophy)Health careOperations managementProcess (computing)MedicineMedical emergencyOperations researchComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

The Health Sciences Centre (HSC) in Winnipeg is the major trauma centre serving the entire province of Manitoba, Northwestern Ontario, and Nunavut. Therefore, it has to handle a high volume of both elective and emergent surgical patients. Because the facility always strives to provide quality care in a fast and effective manner, it initiated a research project to analyze its surgical patient flow and generate ideas on how it could be redesigned to improve the systems performance. After a year of careful analysis, all of the problems identified were grouped into six major categories, showcasing how various departments are affected by each problem. Based on this analysis, the true impact of each problem in the surgical patient flow process can be understood. Steps can now be taken to identify which problems need to be addressed, what changes should be made, and how this will benefit the entire system.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.289
Teacher spread0.256 · 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