Bibliographic record
Abstract
BACKGROUND: Several studies have described pain prevalence, risk factors, pain and medical variables in spinal cord injury (SCI) populations. In this study on traumatic SCI in Turkey, we surveyed the neuropathic pain experiences during in-patient rehabilitation and defined the relationships between neuropathic pain and demographic and SCI characteristics of patients. OBJECTIVES: To survey the neuropathic pain experiences during in-patient rehabilitation in traumatic SCI and to define the relationships between neuropathic pain and demographic and SCI-related characteristics of patients. STUDY DESIGN: Descriptive study. SETTING: Physicial Medicine and Rehabilitation inpatient clinic, Ankara, Turkey METHODS: Sixty-nine SCI patients as inpatients were included in this descriptive study. All patients demographic and SCI-related characteristics were enrolled. The diagnosis of neuropathic pain was made with the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) Pain Scale. Location of pain and pain description, relation to time and severity according to McGill Pain Questionnaire (MPQ) were enrolled. RESULTS: The neuropathic pain localization was below the lesion level in 67 (97.1%) and at the lesion level in 2 (2.9%) patients. The pain was at the hip and leg regions in 36 (52.2%) patients. The neuropathic pain was defined as burning in 27 (39.1%), aching in 26 (37.7%), sharp in 4 (5.8%), stinging in 3 (4.3%), and cramping in 3 (4.3%). We did not find a significant difference between demographic and SCI-related characteristics and the localization of neuropathic pain for the patients (P > 0.05). There was no significant difference according to pain description by MPQ and pain localization (P > 0.05). We found a significant relationship between the patient's lesion level and the region of pain (P < 0.05). CONCLUSION: We found the neuropathic pain due to SCI to be mostly below the lesion level with a burning or aching character and we did not find a significant relationship between the demographic and SCI-related characteristics of the patient and the pain characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".