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Record W4394184454 · doi:10.6084/m9.figshare.11945487

Additional file 2 of Home-based rehabilitation using a soft robotic hand glove device leads to improvement in hand function in people with chronic spinal cord injury:a pilot study

2020· dataset· en· W4394184454 on OpenAlexaboutno aff
Bethel Osuagwu, Sarah Timms, Ruth Peachment, Sarah Dowie, Helen Thrussell, Susan Cross, Rebecca Shirley, Antonio Segura‐Fragoso, Julian Taylor

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsSpinal cord injuryRehabilitationPhysical medicine and rehabilitationHand injuryMedicineRobotic handPhysical therapySpinal cordComputer scienceRobotArtificial intelligence

Abstract

fetched live from OpenAlex

Additional file 2 A video of a participant completing a task involving manipulation of a book from Toronto Rehabilitation Institute hand function test. The participant was instructed to pick up the book at the spin and manipulate it; turning the palm up and down while still holding it. The video shows the performance on this task for the participant during the baseline assessment (Initial), after six weeks of using the device (Week 6), after 12 weeks of using the device (Week 12) and during the follow up assessment session (Week 18). The task was more difficult and was performed less smoothly at initial assessment session compared with the other assessment sessions.

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)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.368
Threshold uncertainty score0.901

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.3680.126

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 head0.031
GPT teacher head0.296
Teacher spread0.265 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

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