MétaCan
Menu
Back to cohort
Record W4295559670 · doi:10.1177/20556683221126994

Integrated fan cooling of the lower back for wheelchair users

2022· article· en· W4295559670 on OpenAlexfundno aff
Katy Griggs, Frederique J. Vanheusden

Bibliographic record

VenueJournal of Rehabilitation and Assistive Technologies Engineering · 2022
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent University
KeywordsThermal sensationSkin temperatureWheelchairSensationBlock (permutation group theory)Heart rateSimulationPhysical medicine and rehabilitationThermal comfortMedicineEngineeringComputer scienceMathematicsPsychologyPhysicsBiomedical engineeringMeteorology

Abstract

fetched live from OpenAlex

Introduction: A large proportion of a wheelchair user's body is in contact with their wheelchair. Integrated fan cooling systems fitted to a wheelchair's backrest aim to alleviate the build-up of heat at the skin-chair interface. The aim of this pilot study was to evaluate the effectiveness of an integrated fan cooling system at cooling the user during daily pushing activity. Methods: Eight male able-bodied participants completed two conditions, with (FAN) and without (CON) fan cooling, pushing for four 15 min blocks. The fan was turned on (highest setting) at the end of block 1 (FAN), whilst in CON the fan remained off. Skin temperature was measured over the back and chest throughout alongside heart rate and perceptual responses (rating of perceived exertion, thermal sensation, thermal comfort, wetness sensation) at the end of each 15 min block. Results: < 0.02), with the difference in lower back skin temperature between the two conditions being 2.20°C at the end of block 4. Conclusion: The integrated fan cooling system provided significant cooling to the lower back without affecting any other physiological or perceptual response, besides wetness sensation.

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)0.003
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 score0.554
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 head0.019
GPT teacher head0.298
Teacher spread0.279 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Rehabilitation and Assistive Technologies EngineeringSame topicSpinal Cord Injury ResearchFrench-language works237,207