Peak alpha frequency as a candidate biomarker of pain sensitivity: the importance of distinguishing slow from slowing
Bibliographic record
Abstract
The study by Valentini et al. (2022) observed that the peak alpha frequency (PAF) of participants became slower after they were exposed to painful, as well as non-painful but unpleasant stimuli. The authors interpreted this as a challenge to our previous studies which propose that the speed of resting PAF, independently of pain-induced changes to PAF, can be a reliable biomarker marker for gaging individual pain sensitivity. While investigations into the role that PAF plays in pain perception are timely, we have some concerns about the assumptions and methodology employed by Valentini et al. Moreover, we believe the authors here have also misrepresented some of our previous work. In the current commentary, we detail the critical differences between our respective studies, with the ultimate aim of guiding future investigations.
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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 | 0.023 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".