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Record W4386699201 · doi:10.56885/zotk98182

The Saskatchewan Lower Extremity Wound Pathway: A Missed Opportunity

2023· article· en· W4386699201 on OpenAlexaboutno aff
Nicholas Peti, Jennifer Culig, Erin Peti

Bibliographic record

VenueLimb Preservation Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCare pathwayAmputationCritical pathwaysWound careClinical pathwayLower limbSurgeryNursingHealth careBusiness

Abstract

fetched live from OpenAlex

In 2013, the Lower Extremity Wound Pathway (LEWP) was one of two provincial pathways initiated in Saskatchewan to standardize patient care and remove surgical bottlenecks. The multidisciplinary pathway was led by vascular surgery and family medicine. The result of the pathway was a significant increase in wound referrals to vascular surgery, with a trend towards decreased major limb amputation. However, this led to increased demand in one tertiary hospital in Saskatoon, ultimately overwhelming local hospital resources and leading to the suspension of the pathway.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.445
Threshold uncertainty score0.896

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0080.002
Scholarly communication0.0070.003
Open science0.0020.006
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0200.003

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 head0.054
GPT teacher head0.285
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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