The effect of pedometer‐supported walking and telemonitoring after disc hernia surgery on pain and disability levels and quality of life
Bibliographic record
Abstract
AIM: The aim of this study was to determine the effect of pedometer-supported walking and telemonitoring after lumbar disc hernia surgery on pain and disability levels and quality of life. METHODS: This was a randomized controlled trial with two randomly selected groups conducted between March 2018 and January 2019. Sixty-seven participants (33 in the intervention group and 34 in the control group) who had undergone lumbar microdiscectomy were allocated to receive and not to receive walking exercise. Pain and disability levels and quality of life of groups were tested with the McGill Pain Questionnaire, the Oswestry Disability Index and the 36-Item Short Form Survey. Measurements were taken 3 weeks after surgery and following completion of the first, second and third months. RESULTS: Compared with the control group, pain level at the first and second months and disability level at the second and third months in the intervention group were significantly lower (p < 0.05), and in the third month, subdimension scores of quality of life (the physical role difficulty, energy and vitality, mental health, social functionality and pain) were higher (p < 0.05). CONCLUSIONS: Walking after herniated disc surgery decreased pain and disability levels and increased the quality of life; nurses can encourage adherence to walking as an effective intervention.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".