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Record W4206611799 · doi:10.2217/cer-2021-0258

Early mobilization in enhanced recovery after surgery pathways: current evidence and recent advancements

2022· article· en· W4206611799 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2022
Typearticle
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsPrehabilitationMedicineMobilizationPerioperativeIntensive care medicinePhysical medicine and rehabilitationAdverse effectPhysical therapySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Early mobilization is a crucial component of enhanced recovery after surgery (ERAS) pathways that counteract the adverse physiological consequences of surgical stress and immobilization. Early mobilization reduces the risk of postoperative complications, accelerates the recovery of functional walking capacity, positively impacts several patient-reported outcomes and reduces hospital length of stay, thereby reducing care costs. Modifiable barriers to early mobilization include a lack of education and a lack of resources. Education and clinical decision-making tools can improve compliance with ERAS mobilization recommendations and create a culture that prioritizes perioperative physical activity. Recent advances include real-time feedback of mobilization quantity using wearable technology and combining ERAS with exercise prehabilitation. ERAS guidelines should emphasize the benefits of structured postoperative mobilization.

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.

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)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.184
GPT teacher head0.439
Teacher spread0.255 · 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