MétaCan
Menu
Back to cohort
Record W3044544102 · doi:10.1080/17549507.2020.1784278

Development and evaluation of the Basic Outcome Measure Protocol for Aphasia (BOMPA)

2020· article· en· W3044544102 on OpenAlexafffund
Aura Kagan, Nina Simmons‐Mackie, Elyse Shumway, J. Charles Victor, Lisa Chan

Bibliographic record

VenueInternational Journal of Speech-Language Pathology · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Toronto
FundersSpeech-Language and Audiology Canada
KeywordsProtocol (science)AphasiaMeasure (data warehouse)Outcome (game theory)Computer sciencePsychologyMedicineCognitive psychologyData miningMathematicsPathologyAlternative medicine

Abstract

fetched live from OpenAlex

PURPOSE: The Basic Outcome Measure Protocol for Aphasia (BOMPA) is a practical tool that allows for a quick self-report on quality of life from the perspective of the person with aphasia, as well as a clinical evaluation of aphasia severity and the ability to participate in conversation. The primary aim of this paper is to describe development of BOMPA and report on results of an inter-rater reliability study involving speech-language pathology raters. METHOD: The inter-rater reliability study utilised a fully crossed design and included independent ratings of 12 videos by 20 speech-language pathologists. RESULT: Results indicate moderate to strong inter-rater reliability among participant speech-language pathology raters (0.65-0.96), as well as when comparing these participant ratings with an expert rater's gold standard (0.59-0.86). CONCLUSION: BOMPA may be a useful outcome measurement tool for time-pressed clinicians in clinical settings.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.160
metaresearch head score (Gemma)0.176
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.160
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1600.176
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0050.003
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0030.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0110.006

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 head0.152
GPT teacher head0.411
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations7
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Speech-Language PathologySame topicNeurobiology of Language and BilingualismFrench-language works237,207