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Record W2997625457 · doi:10.1186/s12883-019-1543-8

Cognitive mediated eye movements during the SDMT reveal the challenges with processing speed faced by people with MS

2019· article· en· W2997625457 on OpenAlexafffund
Bennis Pavisian, Viral Patel, Anthony Feinstein

Bibliographic record

VenueBMC Neurology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
FundersMultiple Sclerosis SocietyMultiple Sclerosis Society of Canada
KeywordsNeurologyNeurochemistryEye movementCognitionPsychologyCognitive psychologyNeurosurgeryVisual processingMedicineNeurosciencePsychiatryPerception

Abstract

fetched live from OpenAlex

Abstract Background The Symbol Digit Modalities Test (SDMT) is regarded as the cognitive test of choice for people with MS (pwMS). While deficits are linked to impaired processing speed, the mechanisms by which they arise are unclear. Cognitive-mediated eye movements offer one putative explanation. The objective of this study was to determine the association between eye movements and performance on the SDMT. Methods Thirty-three people with confirmed MS and 25 matched healthy control subjects (HC) were administered the oral SDMT while eye movements were recorded. Results Mean SDMT scores were significantly lower in pwMS ( p < 0.038). Shorter mean saccade distance in the key area ( p = 0.007), more visits to the key area per response ( p = 0.014), and more total number of fixations in the test area ( p = 0.045) differentiated pwMS from HCs. A hierarchical regression analysis revealed that the number of visits to the key area per response ( p < 0.001; ΔR 2 = 0.549) and total number of fixations in the test area (p < 0.001; ΔR 2 = 0.782) were the most robust predictors of SDMT scores. Conclusion Cognitive-mediated eye movements help elucidate the processing speed challenges confronted by people with MS. Mechanistic insights such as these can potentially help inform new cognitive rehabilitation strategies.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.431

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 communication0.0000.000
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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

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