Investigating the intra-session reliability of short and long latency afferent inhibition
Bibliographic record
Abstract
Objective: To establish the intrasession relative and absolute reliability of Short (SAI) and Long-Latency Afferent Inhibition (LAI). These findings will allow us to guide future explorations of changes to these measures. Methods: 31 healthy individuals (21.06 ± 2.85 years) had SAI and LAI obtained thrice at 30-minute intervals in one session. To identify the minimum number of trials required to reliably elicit SAI and LAI, relative reliability was assessed at running intervals of 5 trials. Results: SAI had moderate-high, and LAI had high-excellent relative reliability. Both SAI and LAI had high amounts of measurement error. LAI had high relative reliability when only 5 frames of data were included, whereas SAI required ∼20-30 frames of data for the same. For both SAI and LAI, individual smallest detectable change was large but was reduced at the group level. Conclusions: SAI and LAI can be used for both diagnostic purposes and to assess group level change but have limited utility in assessing within-individual changes. Significance: These results can be used to inform future work regarding the utility of SAI and LAI, particularly in terms of their ability to identify particularly high or low values of afferent inhibition.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".