MétaCan
Menu
Back to cohort
Record W2062572030 · doi:10.1142/s0218957710002521

A MOBILE COMPUTING TOOL FOR COLLECTING CLINICAL OUTCOMES DATA FROM SHOULDER PATIENTS

2010· article· en· W2062572030 on OpenAlexaboutno aff
Chester Chan, Edward Sihler, Theresa Guckian Kijek, Bruce S. Miller, Richard E. Hughes

Bibliographic record

VenueJournal of Musculoskeletal Research · 2010
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsRotator cuffData collectionMobile deviceAccelerometerReliability (semiconductor)MedicineComputer sciencePhysical therapyPhysical medicine and rehabilitationMedical physicsSurgeryWorld Wide Web

Abstract

fetched live from OpenAlex

The collection of outcomes data is critical for conducting clinical studies in orthopaedic surgery. Both subjective outcome data [e.g. Short Form-12 (SF-12) and Western Ontario Rotator Cuff (WORC) index] and objective data (e.g. range of motion) are necessary. Numerous studies have been conducted on the collection of patient survey data through electronic means (e.g. personal digital assistant and tablet PC), but none of these studies have made use of a device with an intuitive touch-screen interface. Studies have also been conducted on the collection of physical examination data through research-grade accelerometers but few have focused on the use of commercially available electronic devices. The goal of our project was to develop a mobile computing touch-screen system for capturing subjective and objective outcome data for the assessment of patients with rotator cuff tears. We were able to accomplish this goal through the development of a novel iPad/iPod Touch tool. Intra-rater and inter-rater reliability of shoulder flexion and external rotation measurements were good.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.071
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.008
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 science0.0010.000
Research integrity0.0000.002
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 head0.214
GPT teacher head0.545
Teacher spread0.330 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Musculoskeletal ResearchSame topicShoulder Injury and TreatmentFrench-language works237,207