Replication data for: Eye tracking and machine learning to assess cognitive impairment in post-COVID-19 patients
Bibliographic record
Abstract
Participants with Post-COVID condition taking part in the Nautilus study (ClinicalTrials.gov ID: NCT05307575) and the Rehab COVID (ClinicalTrials.gov ID: NCT05846126) recruited by the Consorci Sanitari de Terrassa (Terrassa, Barcelona, Spain) between November 2021 and February 2024 were invited to participate. The protocol was approved by the Drug Research Ethics Committee (CEIm) of Consorci Sanitari de Terrassa (02-20-107-070) and designed in accordance with the Declaration of Helsinki. This dataset contains eye-tracking and neuropsychological assessment data collected from individuals with post-COVID-19 condition (PCC) as part of a study exploring the relationship between oculomotor metrics and cognitive performance. Eye movement data were recorded using the EyeLink 1000 Plus system (SR-Research Ltd., Ottawa, Canada), and are provided in .edf file format. Files beginning with “0” correspond to the following tasks: Fixation Smooth Pursuit Pupil Light Reaction Files beginning with “s” contain raw data for the saccade tasks, which include multiple paradigms. Stimulus presentation was randomized per participant. A .mat file is provided containing: The order of paradigm presentation The order of stimulus presentation Information about stimulus amplitude, direction, and inter-stimulus timing, all of which were randomized. Neuropsychological Data An accompanying .xlsx file includes participants’ scores from a battery of neuropsychological tests: Digit Symbol Test Digit Span Backward Test Trail Making Test (Parts A and B) Stroop Color and Word Test Controlled Oral Word Association Test (COWAT)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.026 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".