Manual therapy interventions in the treatment of plantar fasciitis: A comparison of three approaches
Bibliographic record
Abstract
BACKGROUND: Plantar fasciitis is one of the common causes of heel pain and a common musculoskeletal problem often observed by clinicians. Numerous options are available in treating plantar fasciitis conservatively, but no previous studies have compared combined conservative management protocols. AIM: The aim of this study was to compare manipulation of the foot and ankle and cross friction massage of the plantar fascia; cross friction massage of the plantar fascia and gastrocsoleus complex stretching; and a combination of the aforementioned protocols in the treatment of plantar fasciitis. SETTING: This study was conducted at the University of Johannesburg, Chiropractic Day Clinic, and included participants that complied with relevant inclusion criteria. METHODS: Forty-five participants between the ages of 18 and 50 years with heel pain for more than 3 months were divided into three groups and received one of the proposed treatment interventions. The data collected were range of motion (ROM) of the ankle (using a goniometer) and pain perception using the McGill Pain Questionnaire and Functional foot index and algometer. RESULTS: The results of this study indicate that cross friction massage of the plantar fascia and stretching of the gastrocsoleus complex showed the greatest overall improvement in terms of reducing the pain and disability and ankle dorsiflexion ROM, whereas the combination group showed the greatest increase in plantar flexion. CONCLUSION: The results demonstrated that all three protocols had a positive effect on the ROM and pain perception to patients with plantar fasciitis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".