An audiometric study of the effects of paraspinal stimulation on hearing acuity in human subjects understanding the Harvey Lillard phenomenon
Bibliographic record
Abstract
BACKGROUND: The founder of chiropractic, Daniel David Palmer, constructed a model of causation of disease based on his seminal experience with a patient, Harvey Lillard, who lost his hearing at the instant of injuring his upper back, but had his hearing restored suddenly 17 years later after receiving spinal manipulation. Palmer's model of disease causation, that of displaced vertebrae impinging on spinal nerves and thereby disrupting the innervation of dependent organs, was in fact incongruent with what was known at the time about human neuroanatomy and neurophysiology. The current study proposes and tests an alternative hypothesis: that increased afferent input from paraspinal muscles attenuates the central transmission of auditory information. METHODS: Between September 13 and November 13, 2013, forty healthy young adults were recruited and randomly divided into two cohorts: one receiving successive trials of sham TENS, and the second receiving sham and then authentic TENS. During the administration of sham and authentic TENS to the upper thoracic spine, hearing acuity was measured to determine perception thresholds at the frequencies normally tested clinically. RESULTS: In the first cohort, there were no differences in perception thresholds in the first and second trials of sham TENS, speaking to the reliability of the testing process. In the second cohort, there were no significant differences in perception thresholds during sham and authentic TENS. CONCLUSIONS: Within the constraints of the current study design, including demographic characteristics and TENS parameters, there was no evidence that innocuous afferent input to upper thoracic paraspinal muscles modulated thresholds of audibility.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".