COMPARISON OF FOOT ORTHOSIS, PHYSIOTHERAPY AND NIGHT SPLINTS IN THE TREATMENT OF PLANTAR FASCIITIS
Bibliographic record
Abstract
PURPOSE: To determine the effectiveness of physiotherapy, foot orthosis prescription and night splint treatment for previously untreated plantar fasciitis. METHODS: This single blinded, randomized clinical trial recruited 50 females (mean ± SD, age: 43.1 ± 10.5y; height: 165.3 ± 7.3cm; weight: 80.2 ± 17.7kg) and 47 males (mean ± SD, age: 46.2 ± 9.6y; height: 179.8 ± 7.2cm; weight: 88.9 ± 16.3kg) who were diagnosed with plantar fasciitis (rheumatologic conditions excluded) and had no previous treatment for their heel pain, except for the use of NSAID's. Patients were randomized to one of the following treatment groups: (Group 1) 10 physiotherapy sessions (n = 31 with 43 limbs); (Group 2) foot orthosis prescription (n = 22 with 41 limbs); (Group 3) night splint use (n = 33 with 42 limbs). All subjects were taught home soleus and gastrocnemius stretching exercises. Pain was assessed using visual analog scales (VAS) for morning, walking, sitting and physical activity pain. RESULTS: The VAS scores (mean and SD) for baseline and 12 weeks follow-up are presented in the Table below. CONCLUSION: Each treatment appears equally efficacious in treating previously untreated plantar fasciitis. This project was funded by Alberta Heritage Foundation for Medical Research-Health Research Fund and Colman Prosthetics & Orthotics.Table: No Caption Available
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".