P-907 - Escitalopram monotherapy in managing depressive and pain symptoms in patients with depression and fibromyalgia
Bibliographic record
Abstract
Fibromyalgia is characterized by diffuse musculoskeletal pain, fatigue, sleep disturbance, headaches, and cognitive and affective disturbance. The aetiological pathway is not clearly understood nor is there a clearly defined pathophysiological profile for the disorder that distinguishes it as a diagnosis. To investigate the efficacy of 20 mg escitalopram monotherapy in management of depressive symptoms, pain, fatigue and sleep. To demonstrate efficacy of escitalopram in improving depressive and pain symptoms as measured by primary and secondary outcome measures. 60 subjects were randomized to treatment or control group and evaluated at 4,8 and 12 weeks using MADRAS, McGill pain questionnaire, fibromyalgia impact score and sleep questionnaires. Mean MADRAS score was significantly lower in the treatment group (14.96 ± 1.02 vs 18.12 ± 1.05; p = 0.03) as compared to the control group. When the change from baseline on MADRAS score was compared between the groups, the reduction was even more apparent in the treatment group (−12.49 ± 1.48 vs − 4.77 ± 1.54; p = 0.001).Mean FIBROMYALGIA score was significantly lower in the treatment group as compared to the control group (54.20 ± 2.09 vs 64.93 ± 2.14; p < 0.001). The reduction was much higher in the treatment group than the control group (−21.31 ± 2.94 vs − 2.79 ± 3.03; p < 0.001). Positive statistically significant results for treatment group on all primary outcome measures of MADRAS and fibromyalgia impact score questionnaire.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".