A Method to Assess Neurological Effectiveness of a Spinal Adjustment for an Individual Patient: A Descriptive Study
Bibliographic record
Abstract
Introduction: A hallmark in health care research is comparison, typically done by comparing groups of patients, e.g., intervention group versus no intervention group. The clinician may be interested in bringing these research methods to the level of the individual patient in practice. Such is done in the present study, where the neurological indicator of resting pulse rate (RPR) is compared pre versus post spinal adjustment, and also compared to instances of no adjustment – for one individual patient. Research indicates that a lower RPR is healthier than a higher RPR. Methods: Neurological disturbance was operationally defined in the present study as at least two increases in RPR on consecutive visits. Based on this criterion, the patient, over hundreds of RPR measures observed over approximately 2 years, had 16 instances of neurological disturbance; in one of these instances a chiropractic spinal adjustment was given. The 15 other instances were used to estimate a predicted post RPR, which was compared to the observed post-adjustment RPR. Results: Post-adjustment RPR was 67.5 beats per minute (BPM) which was only slightly lower than the average predicted post RPR of 68.1 BPM. Conclusion: The method described may help clinicians determine if their intervention was neurologically effective. The method also provides normative RPR data for future comparisons of adjustment versus no adjustment. In the present case, the chiropractic adjustment post RPR was better (lower) than the predicted post RPR, but only slightly so.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".