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Record W2128794539 · doi:10.1080/03093640601044311

The Lower Limb Amputee Measurement Scale

2007· article· en· W2128794539 on OpenAlexaff
Oren Cheifetz, Mark Bayley, Sharon Grad, Debbie Lambert, Cass Watson, Kelly L. Minor

Bibliographic record

VenueProsthetics and Orthotics International · 2007
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsRehabilitationReliability (semiconductor)Physical therapyPhysical medicine and rehabilitationMedicineLower limbPredictive validityConcurrent validityPearson product-moment correlation coefficientProspective cohort studySurgeryStatisticsMathematicsPatient satisfaction

Abstract

fetched live from OpenAlex

This study assesses the reliability and predictive validity of the Lower Limb Extremity Amputee Measurement Scale (LLAMS), which is an assessment tool designed to predict the length of stay (LOS) of patients with lower limb amputations in a rehabilitation program. In order to evaluate inter-rater reliability a prospective evaluation was completed by five independent evaluators (n = 10). Predictive validity was evaluated retrospectively by comparing the LLAMS predicted LOS to actual LOS (n = 147). The ability of the amputee team members to administer the LLAMS to patients was very high (ICC [2,1] = 0.98, CI 95% = 0.96 - 0.99, F[9, 36] = 78.71, p < 0.05). In addition, a moderate positive correlation was found between the LLAMS predicted LOS and the actual LOS (Pearson Correlation Coefficient, r = 0.465, p < 0.01), and the LLAMS was able to identify those patients who required short versus long rehabilitation stays. The incorporation of the LLAMS into the physiatrist's initial assessment of patients in the amputee clinic has enhanced the ability to manage better the LOS and the time patients wait to enter the rehabilitation program.

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)0.001
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 score0.575
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.275
Teacher spread0.258 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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