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Record W2889236635 · doi:10.22374/cjgim.v13i3.244

The Value of a Rapid Access Internal Medicine Clinic – An Observational Perspective

2018· article· en· W2889236635 on OpenAlexaffvenueabout
Ryan Joseph LeBlanc, Karmen Jongewaard, Laura Farrell

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of British ColumbiaVancouver Coastal HealthIsland Health
Fundersnot available
KeywordsMedicineObservational studyEmergency medicineMedical emergencyFamily medicineEmergency departmentNursingInternal medicine

Abstract

fetched live from OpenAlex

Background Rapid access Internal Medicine (IM) clinics aim to reduce burden on inpatient services. Despite an increased prevalence of these clinics across Canada, there is a lack of evidence demonstrating their value. Methods An observational retrospective review was undertaken to identify the usage of our IM clinic. A prospective analysis of Internal Medicine Clinical Teaching Unit (CTU) diverted admissions and a subsequent cost benefit analysis was performed. Results Referrals were primarily from emergency room physicians (47%) and general practitioners (34%). Of the requests for admission over a 4-week period, 6.1% were diverted with clinic follow-up within four days. A potential $30,000 of inpatient care costs were prevented over the study period. Conclusion Rapid access IM clinics help reduce demand on emergency departments and inpatient services. A significant percentage of hospital admissions may be avoided by implementing rapid access clinics. Further study is needed to better quantify the overall benefit.

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)0.006
metaresearch head score (Gemma)0.040
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.118
GPT teacher head0.413
Teacher spread0.295 · 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

Citations1
Published2018
Admission routes3
Has abstractyes

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