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Record W2942001310 · doi:10.3233/978-1-61499-951-5-170

Using a Markov Chain Model to Analyze the Relationship Between Avoidable Days and Critical Care Capacity

2019· article· en· W2942001310 on OpenAlexaff
Gurmeet Singh

Bibliographic record

VenueStudies in health technology and informatics · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsUniversity of AlbertaUniversity of Victoria
Fundersnot available
KeywordsMarkov chainMetric (unit)StressorMarkov modelPopulationMedicineStatisticsEmergency medicineComputer scienceOperations managementMathematicsEngineeringEnvironmental health

Abstract

fetched live from OpenAlex

Hospital capacity strain is ubiquitous, and a significant stressor in critical care. Avoidable days (AD) are frequently used as a metric of capacity. Using a Markov chain model, we studied the relationship between AD and surgical cancellations in a cardiovascular ICU. The model varied the probability of discharging a patient to study this effect over a pool of 108 simulated patients with length of stay data reflecting the actual population. The model behaved as expected with decreasing AD with increasing probability of patient discharge. However, there was no effect on the surgical cancellation rate. We conclude that there is no relationship between AD and critical care capacity as measured by surgical cancellation rate.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.271
GPT teacher head0.496
Teacher spread0.225 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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