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Record W2098364480 · doi:10.1093/ptj/82.4.364

Reliability of Safe Maximum Lifting Determinations of a Functional Capacity Evaluation

2002· article· en· W2098364480 on OpenAlexaff
Douglas P. Gross, Michele C. Battié

Bibliographic record

VenuePhysical Therapy · 2002
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInter-rater reliabilityIntraclass correlationReliability (semiconductor)Confidence intervalPsychologyPhysical therapyTest (biology)MedicineStatisticsPsychometricsClinical psychologyMathematicsRating scaleDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Functional capacity evaluations (FCEs) are measurement tools used in predicting readiness to return to work following injury. The interrater and test-retest reliability of determinations of maximal safe lifting during kinesiophysical FCEs were examined in a sample of people who were off work and receiving workers' compensation. SUBJECTS: Twenty-eight subjects with low back pain who had plateaued with treatment were enrolled. Five occupational therapists, trained and experienced in kinesiophysical methods, conducted testing. METHODS: A repeated-measures design was used, with raters testing subjects simultaneously, yet independently. Subjects were rated on 2 occasions, separated by 2 to 4 days. Analyses included intraclass correlation coefficients (ICCs) and 95% confidence intervals. RESULTS: The ICC values for interrater reliability ranged from.95 to.98. Test-retest values ranged from.78 to.94. DISCUSSION AND CONCLUSION: Inconsistencies in subjects' performance across sessions were the greatest source of FCE measurement variability. Overall, however, test-retest reliability was good and interrater reliability was excellent.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.399

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 head0.065
GPT teacher head0.320
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

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

Citations119
Published2002
Admission routes1
Has abstractyes

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