Clinical topographic analysis of neuropathic pain in patients admitted in a center of multidisciplinary treatment
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Verbal investigation is a critical step of nursing neurological evaluation of neuropathic pain patients, due to its multidimensionality. There are few studies in the literature specifically dealing with this subject. In light of the above, this study aimed at evaluating medical records on clinical topographic characteristics of neuropathic pain reported by patients from a multidisciplinary management center. METHODS: This is a documental, crossover and quantitative study evaluating 50 medical records of patients with established neuropathic pain diagnosis who came for routine consultations between January and June 2014. Data collection form was based on McGill Pain Questionnaire and data regarding age, gender, pain topography and presence of verbal descriptors were analyzed. Data were submitted to statistical analysis and Chi-square test was applied to compare association among variables. RESULTS: There has been prevalence of females (64%), with mean age of 57 years. Most common pain descriptors were from the sensory dimension and were associated to cases where neuropathy affected lower limbs (p=0.006). CONCLUSION: There has been association between topography and pain dimension. Due to the subjectivity and complexity involving neuropathic pain evaluation, it is necessary to understand its clinical manifestations and to prepare the whole multidisciplinary team, especially Nursing, which plays a critical role in verbal investigation of painful patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".