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Record W4312173246 · doi:10.3390/jcm12010093

The Usefulness of Optical Coherence Tomography in Disease Progression Monitoring in Younger Patients with Relapsing-Remitting Multiple Sclerosis: A Single-Centre Study

2022· article· en· W4312173246 on OpenAlexaboutno aff
Magdalena Torbus, Ewa Niewiadomska, Paweł Dobrakowski, Ewa Papuć, Barbara Rybus-Kalinowska, Patryk Szlacheta, Ilona Korzonek-Szlacheta, K Kubicka, Beata Łabuz-Roszak

Bibliographic record

VenueJournal of Clinical Medicine · 2022
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsnot available
FundersŚląski Uniwersytet Medyczny w KatowicachŚląski Uniwersytet Medyczny
KeywordsMedicineMultiple sclerosisExpanded Disability Status ScaleRelapsing remittingOptical coherence tomographyAudiologyPediatricsInternal medicineOphthalmologyPsychiatry

Abstract

fetched live from OpenAlex

The purpose of the study was to assess the usefulness of optical coherence tomography (OCT) in the detection of the neurodegenerative process in younger patients with multiple sclerosis (MS). The study group consisted of 61 patients with a relapsing remitting course of MS (mean age 36.4 ± 6.7 years) divided into two groups: short (≤5 years) and long (>10 years) disease duration. OCT, P300 evoked potential, Montreal Cognitive Assessment, and performance subtests (Picture Completion and Digit Symbol) of the Wechsler Adult Intelligence Scale were performed in all patients. Mean values of most parameters assessed in OCT (pRNFL Total, pRNFL Inferior, pRNFL Superior, pRNFL Temporalis, mRNFL, GCIPL, mRNFL+GCIPL) were significantly lower in MS patients in comparison to controls. And in patients with longer disease duration in comparison to those with shorter. Most OCT parameters negatively correlated with the EDSS score (p < 0.05). No significant correlation was found between OCT results and both P300 latency and the results of psychometric tests. OCT, as a simple, non-invasive, quick, and inexpensive method, could be useful for monitoring the progression of disease in MS patients.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.020
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.314
Teacher spread0.249 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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