Understanding How Patients With Lumbar Radiculopathy Make Sense of and Cope With Their Symptoms
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Lumbar radiculopathy, characterized by pain radiating along a nerve root, significantly diminishes the quality of life due to its neuropathic nature. Patients' understanding of their illness and the coping strategies they employ directly influence how they manage their condition. Understanding these illness representations from the patient's perspective is crucial for healthcare providers seeking to optimize treatment outcomes. This study adopted a qualitative interpretive/constructive paradigm to explore this dynamic. A qualitative evidence synthesis approach, utilizing best-fit framework synthesis for data extraction, was applied to analyze primary qualitative studies focused on patient experiences with lumbar radiculopathy. Using SPiDER (Sample, Phenomenon of interest, Design, Evaluation, Research type) to guide the search strategy, extracted data was mapped against the Common-Sense Model of Self-Regulation (CSM) framework. Sixteen studies, with moderate to minor methodological quality concerns, were included in the analysis. Data mapping across CSM domains generated 14 key review findings. Results suggest that patients with high-threat illness representations often exhibit maladaptive coping behaviors (e.g., activity avoidance) driven by emotional responses. In contrast, problem-solving techniques appear to contribute to positive outcomes (e.g., exercise adherence and effective self-management) in patients who perceive their condition as less threatening. These findings highlight the potential benefits of interventions designed to reduce perceived threat levels and enhance self-efficacy in patients with lumbar radiculopathy, leading to improved self-management and ultimately better health outcomes.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".