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Record W4205240135 · doi:10.1017/cjn.2021.476

P.226 Variations in and Determinants of Length of Stay at an Academic Spinal Care Center from 2006-2019

2021· article· en· W4205240135 on OpenAlexaffvenue
Charlotte Dandurand, MN Hindi, T Ailon, Michal Boyd, R Charest-Morin, Nicolas Dea, Marton Dvorak, C. Daniel Fisher, Bum Sun Kwon, Stephanie Paquette, Jessica Street

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2021
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsVancouver Biotech (Canada)
Fundersnot available
KeywordsMedicineInterquartile rangeEpidemiologyDemographicsOvertimeEmergency medicineInternal medicineSurgeryDemography

Abstract

fetched live from OpenAlex

Background: Length of stay (LOS) is a surrogate for care complexity and a determinant of occupancy and service provision. Our primary goal was to assess changes in and determinants of LOS at a quaternary spinal care center. Secondary goals included identifying opportunities for improvement and determinants of future service planning. Methods: This is a prospective study of patients admitted from 2006 to 2019. Data included demographics, diagnostic category (degenerative, oncology, deformity, trauma, other), LOS (mean, median, interquartile range, standard deviation) and in-hospital adverse events (AEs). Results: 13,493 admissions were included. Mean age has increased from 48.4 (2006) to 58.1 years (2019) (p=<0.001). Mean age increased overtime for patients treated for deformity (p=<0.001), degenerative pathology (p=<0.001) and trauma (p=<0.001), but not oncology (p=0.702). Overall LOS has not changed over time (p=0.451). LOS increased in patients with degenerative pathology (p=0.019) but not deformity (p=0.411), oncology (p=0.051) or trauma (p=0.582). Emergency admissions increased overtime for degenerative pathologies (p=<0.001). AEs and SSIs have decreased temporally (p=<0.001). Conclusions: This is the first North American study to analyze temporal trends in LOS for spine surgery in an academic center. Understanding temporal trends in LOS and patient epidemiology can provide opportunities for intervention, targeted at the geriatric populations, to reduce LOS.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.034
GPT teacher head0.308
Teacher spread0.274 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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