El dolor neuropático como causa de ansiedad, depresión y trastornos del sueño en condiciones de práctica médica habitual: resultados del estudio naturalístico DONEGA
Bibliographic record
Abstract
Aim: The goal of this cross-sectional evaluation was to assess pain impact on sleep and symptoms of depression and anxiety in patients with neuropathic pain (NeP). Methods: Participants in an observational, prospective and multicenter study (DONEGA study) with NeP of broad etiologies, completed the Short Form-McGill Pain Questionnaire (SF-MPQ), the COVI Anxiety Scale, the RASKIN Depression Scale, and the MOS Sleep Scale at baseline. Results: A total of 1,519 patients above 18 years [mean ± SD; 56.9±13.6 years old (61.2% female)] with NeP for 1.1±2.8 years were enrolled in the study. Average present pain intensity was 2.8±1.0 (range 0-5) and mean pain past week was 71.2±18.9 mm (range 0-100). Pain substantially interfered with patient normal sleep and its attributes, obtaining high scoring in composite measures (9-items); 47.1±21.3 (range 0-100). The 19.7% and 12.9% of patients had symptoms of depression and anxiety, respectively. Severity of previous and present pain were the most important determinants causing negative impact on patient sleep. Scoring on sleep scale and, alternately, depression and anxiety scales scoring were the main determinants for depression and anxiety, respectively. Conclusions: NeP negatively impact on patient sleep and its attributes, while causes a substantial proportion of patients with symptoms of anxiety and depression. Pain severity amplifies these findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".