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Record W2796631349 · doi:10.5430/jha.v7n3p17

Unicondylar knee arthroplasty in the inpatient vs. outpatient setting: Impact on process time

2018· article· en· W2796631349 on OpenAlexvenueno aff
Ibrahim Mamdouh Zeini, Meghan Hufstader Gabriel, Xinliang Liu, Alice Noblin, Bernardo Ramirez, J. Mandume Kerina

Bibliographic record

VenueJournal of Hospital Administration · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPacuMedicineOutpatient surgeryArthroplastyDemographicsOutpatient visitsInpatient careEmergency medicinePhysical therapyHealth careSurgeryAmbulatoryDemography

Abstract

fetched live from OpenAlex

Objective: There is a lack of research on the impact of transitioning inpatient procedures to the outpatient setting, specifically on process time. Unicondylar knee arthroplasty (UKA) presents an opportunity for further investigation as it is already in the early stages of transitioning to the outpatient setting.Methods: This study analyzed the medical records of 1,075 patients who received UKA from a single surgeon (400 in the outpatient setting and 675 in the inpatient setting). Time in Pre-Op, surgery time, and time in post-anesthesia care unit (PACU) were recorded and compared between inpatient and outpatient settings using Ordinary Least Squares Regression models.Results: Outpatient UKAs outperformed inpatient UKAs across two out of three process time variables even after controlling for comorbidities, social history, demographics, and surgery related characteristics. Actual surgery time was no different between the two settings.Conclusions: This study demonstrated that UKA performed in the outpatient setting is associated substantial time savings preoperatively and postoperatively compared with cases performed in the inpatient setting. More research is needed to compare other outcome measures such as patient outcomes of UKA between the two settings. Implications beyond time savings should consider supply and human resources costs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.202
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.286
Teacher spread0.279 · 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.

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
Published2018
Admission routes1
Has abstractyes

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