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Record W4392121342 · doi:10.2106/jbjs.rvw.23.00208

Mobile Application Use and Patient Engagement in Total Hip and Knee Arthroplasty

2024· article· en· W4392121342 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.

Bibliographic record

VenueJBJS Reviews · 2024
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineArthroplastyTotal knee arthroplastyPhysical therapyTotal hip arthroplastyMEDLINEPhysical medicine and rehabilitationSurgery

Abstract

fetched live from OpenAlex

» Mobile applications (MAs) are widely available for use during the perioperative period and are associated with increased adherence to rehabilitation plans, increased satisfaction with care, and considerable cost savings when used appropriately.» MAs offer surgeons and health care stakeholders the ability to collect clinical data and quality metrics that are important to value-based reimbursement models and clinical research.» Patients are willing to use wearable technology to assist with data collection as part of MAs but prefer it to be comfortable, easy to apply, and discreet.» Smart implants have been developed as the next step in MA use and data collection, but concerns exist pertaining to patient privacy and cost.» The ongoing challenge of MA standardization, validation, equity, and cost has persisted as concerns regarding widespread use.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.446

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 head0.027
GPT teacher head0.295
Teacher spread0.268 · 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