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Record W3127374033 · doi:10.1503/cjs.019519

Combining enhanced recovery and short-stay protocols for hip and knee joint replacements: the ideal solution

2021· article· en· W3127374033 on OpenAlexaffvenueabout
Pascal‐André Vendittoli, Karina Pelleï, Carla Williams, Claude Laflamme

Bibliographic record

VenueCanadian Journal of Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-RosemontCanadian Patient Safety InstituteHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineHealth careOrthopedic surgeryPerioperativePatient safetyMedical emergencyArthroplastyMEDLINEIntensive care medicinePhysical therapySurgery

Abstract

fetched live from OpenAlex

<h3>Summary</h3> Pressure to reduce health care costs, limited hospital bed availability as well as improvements in surgical techniques and perioperative care motivated many health care centres to implement short-stay protocols for patients undergoing hip or knee arthroplasty. To improve patient outcomes and maintain care safety, we strongly believe the best way to implement a successful outpatient program would be to embrace the principles of Enhanced Recovery After Surgery (ERAS), and to improve patient recovery to a level such that the patient could leave the hospital sooner. Enhanced Recovery Canada and the Canadian Patient Safety Institute support the development of ERAS pathways for orthopedic procedures. The goal is to provide patients, health care providers and leaders with helpful tools and resources to effectively implement and sustain ERAS protocols. Reducing the rate of adverse events while reducing the length of hospital stays to less than 24 hours is a winning situation for everyone.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.067
GPT teacher head0.290
Teacher spread0.223 · 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

Citations13
Published2021
Admission routes3
Has abstractyes

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