Improvement of Cervical Lordosis and Reduction of Forward Head Posture with Anterior Head Weighting and Proprioceptive Balancing Protocols
Bibliographic record
Abstract
Background and Objectives: Evidence of the kinesiopathological component of the vertebral subluxation complex is frequently apparent in observation and assessment of posture. Postural distortion from loss of the normal cervical lordosis has been referred to as forward head posture (FHP) and may precipitate pain, decreased ranges of motion and other health problems. FHP can be quantified by measurement of neutral lateral cervical radiographs. The objective of this study was to determine if the use of head weighting and balancing protocols could improve the cervical curvature and head carriage. Methods: One hundred and thirty one patients from six Chiropractic clinics in the United States, two in Canada and one in the Russian Federation participated in the study. Study participants were randomly selected and assessed with neutral lateral cervical radiographs. These patients performed motion activities while wearing three or five pounds of weight on the front of their heads for five minutes then a weighted stress lateral cervical film was taken. Results: A comparison of the measured results from the two films was made considering the cervical lordosis and FHP. Average improvements in the cervical lordosis of 34% (p < .0001) and in FHP 14mm (p < .0001) were noted after the head weighting protocol was preformed with five pounds. Improvement of cervical lordosis of 31% (p < .001) and in FHP 18mm (p < .0001) was recorded in a group using three pounds of weight.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".