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Record W2520902474

PREDICTORS OF PROLONGED LENGTH OF STAY AFTER MAJOR FOOT AND ANKLE SURGERY

2012· article· en· W2520902474 on OpenAlexaff
H. Pakzad, Gowreeson Thevendran, Alastair Younger, Hong Qian, Murray J. Penner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsMedicineAnkleInterquartile rangePerioperativeBody mass indexUnivariate analysisPhysical therapyBayesian multivariate linear regressionMultivariate analysisSurgeryInternal medicineLinear regression
DOInot available

Abstract

fetched live from OpenAlex

Introduction Greater length of stay (LOS) after elective surgery results in increased use of health care resources and higher costs. Within the realm of foot and ankle surgery, improved perioperative care has enabled a vast majority of procedures to be performed as a day surgery. The objective of this study was to determine the perioperative factors that predict a prolonged LOS after elective ankle replacement or fusion. Methods Data was prospectively collected on patients undergoing either an ankle fusion or ankle replacement for end-stage ankle arthritis at our institution (2003–2010). In the analysis, LOS was the outcome and age, sex, physical and mental functional scores, comorbid factors, ASA grades, type and length of operation and body mass index (BMI) were potential perioperative risk factors. Univariate and multivariate generalized linear regression models with gamma distribution and log link function were conducted. Results A total of 336 patients were included in the study. The median LOS was 76 hours with interquartile range of 52.5–97. Using regression analysis, we showed aging, female gender, a higher ASA score, multiple medical comorbidities, rheumatoid arthritis, a lower score in the physical component (PCS) and general health domain (GH) of SF-36, open surgery and an increased length of surgical time were all significantly associated with an increased LOS. Conversely, obesity, the SF-36 Mental Component Score and the date of admission were noninfluential of LOS. A predictive model was also developed using these same risk factors. Conclusions Increased age, female gender, high ASA scores, low SF-36 GH and PCS scores, increased number of medical comorbidities, rheumatoid arthritis and open surgery were all factors that increased LOS significantly after ankle fusion or ankle replacement. This group of patients may warrant better education and more focused perioperative care when it comes to designing care pathways and allocating health care resources.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.235
Teacher spread0.219 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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