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Record W3012634903 · doi:10.1186/s13104-020-04951-4

Medical surveillance unit: patient characteristics, outcome, and quality of care in Saskatchewan, Canada

2020· article· en· W3012634903 on OpenAlexafffundabout
Karl Vantomme, Md Muniruzzaman Siddiqui, Kish Lyster

Bibliographic record

VenueBMC Research Notes · 2020
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSaskatchewan Health AuthoritySaskatchewan HealthUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsMedicineAsthmaPneumoniaAlcohol abuseDiabetes mellitusMortality rateCOPDEmergency departmentPediatricsEmergency medicineIntensive care medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: Intermediate care units provide a high level of care to complex patients and are becoming increasingly popular in North America. Despite the growing popularity of Intermediate care units, very little is known about them. This study explored a typical Intermediate care unit, identifying patient characteristics including demographics, comorbidities, length of stay, as well as primary and secondary diagnosis and mortality. RESULTS: A total of 200 patients chart were reviewed, of which, 102 were male, and 89 patients were younger than 65 years old. Diabetes, hypertension, and chronic obstructive pulmonary disease were common among patients with a prevalence of 33.5%, 56%, and 32.5%, respectively. Alcohol use disorder, asthma, liver disease and IV drug abuse were much more common in patients younger than 65 years. The average length of stay was 5.31 days regardless of age. Almost two-thirds of the patients in the Intermediate care unit were admitted directly from the emergency room. The mortality rate among the patients studied was 9.5%. The most common admitting diagnosis was respiratory diseases such as chronic obstructive pulmonary disease or Pneumonia (38.0%), followed by cardiac disorders which were predominantly arrhythmias and congestive heart failure (27.0%).

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)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
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 head0.166
GPT teacher head0.426
Teacher spread0.260 · 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.

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

Citations2
Published2020
Admission routes3
Has abstractyes

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