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Record W31849816 · doi:10.3389/fneur.2019.01249

Design Piracy: A Constructive Controversy

2009· article· en· W31849816 on OpenAlexaboutno aff
Sara B. Marcketti

Bibliographic record

VenueFrontiers in Neurology · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsConstructivePolitical scienceEpistemologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

<b>Background:</b> X-linked dystonia parkinsonism (XDP) is a neurodegenerative disease endemic to Filipinos with maternal lineage from Panay Island, Philippines. Patients present with dystonia concurrent with or followed by parkinsonism. Non-motor symptoms also predominate, affecting behavior and cognition. We aimed to translate and do cross-cultural adaptation and validation of the Montreal Cognitive Assessment Tool (MoCA) into Hiligaynon (MoCA-Hil), the language spoken in Panay Island, to perform baseline cognitive screening of XDP patients. <b>Methods:</b> Forward translation to Hiligaynon was done by two translators, then back translation of a single version was adapted and approved by a committee. A pilot testing was done yielding the final translated version, which was then tested on 46 XDP patients. The test-retest reliability was measured for 11 patients. The XDP-MDSP (Movement Disorder Society of the Philippines) rating scale was used to assess disease severity. <b>Results:</b> The MoCA-Hil showed an acceptable test-retest reliability [intraclass correlation (ICC) 0.74] and internal consistency (Cronbach's alpha 0.86 at baseline, 0.81 at 12 weeks). The two subscales with low ICC at 0.09 and 0.21 were delayed recall and orientation, respectively. <b>Conclusion:</b> Translation, cultural adaptation and validation of the MoCA to Hiligaynon was successfully done. This tool may now be used in clinical practice and in research for Hiligaynon-speaking subjects.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.523

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 communication0.0000.001
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 head0.015
GPT teacher head0.197
Teacher spread0.182 · 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 designNot applicable
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

Citations0
Published2009
Admission routes1
Has abstractyes

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