Bibliographic record
Abstract
Neurophysiological signals detected non-invasively at the scalp using techniques such as EEG/MEG contain rhythms (oscillatory activity) occurring at distinct frequency bands.The predominant rhythm in healthy humans occurs between the bandwidth of 8-14 Hz, and is commonly referred to as the alpha rhythm.The frequency within the alpha band where power is maximal is called the peak alpha frequency (PAF) or individual alpha frequency.PAF varies considerably between individuals [10; 15] and this variability is believed to play a role in the individual differences in perceptual ability and temporal binding of sensory information [1; 9; 21; 23].The relationship of PAF to both acute and chronic pain is an exciting area currently under investigation by multiple laboratories.Patients with chronic pain often exhibit changes in alpha rhythms, particularly slower PAF, when compared to control subjects [26], and the degree of PAF slowing has been found to correlate with chronic pain duration [4].One interpretation of this PAF slowing is that it reflects brain processes that actively maintain chronic pain, such as excess "inhibition or disfacilitation" brought about by ongoing pain [16].An alternative interpretation is that slower PAF reflects processes related to higher pain sensitivity and an increased disposition to develop chronic pain that predate disease onset.This latter interpretation is supported by a series of studies of acute pain in heathy subjects, which found that an individual's PAF recorded during a pain-free rest period was negatively correlated with the intensity of a future pain event [6][7][8].This work has undergone initial clinical validation: in a cohort of patients undergoing thoracotomy, PAF collected prior to surgery was able to predict the severity of post-surgery pain [20].Thus, PAF has been suggested as a biomarker of pain sensitivity which has been hypothesized to be a key factor in the transition from acute to chronic pain [11].Precisely how PAF fits into the myriad of biological, social, and psychological factors involved in the transition from acute to chronic pain has remained unexplored.One of these
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".