Insomnia associated with neutrophil/lymphocyte ratio in female patients with schizophrenia
Bibliographic record
Abstract
Introduction Worse sleep quality and increased inflammatory markers in women with schizophrenia (Sch) have been reported (Lee et al. 2019). However, the physiological mechanisms underlying the interplay between sleep and the inflammatory pathways are not yet well understood (Fang et al. 2016). Objectives Analyze the relationship between Neutrophil/Lymphocyte (NLR), Monocyte/Lymphocyte (MLR) and Platelet/Lymphocyte (PLR) ratios, and insomnia in Sch stratified by sex. Methods Final sample included 176 Sch patients (ICD-10 criteria) [mean age: 38.9±13.39; males: 111(63.1%)]. Assessment: PANSS, Calgary Depression Scale (CDSS), and Oviedo Sleep Questionnaire (OSQ) to identify a comorbid diagnosis of insomnia based on ICD-10. Fasting counting blood cell were performed to calculate ratios. Statistics: U Mann-Whitney, logistic regression. Results Insomnia as comorbid diagnosis was present in 22 Sch (12.5%) with no differences between sex [14 males (12.6%), 8 females (12.3%)], neither in their age. Female patients with insomnia showed increased NLR [2.44±0.69 vs. 1.88±0.80, U=122.00 (p=0.034)]. However, no differences in PLR and MLR were found, neither in any ratio in males. Regression models using insomnia as dependent variable and covariates (age, PANSS-positive, PANSS-negative, CDSS) were estimated. Females: presence of insomnia was associated with NLR [OR=3.564 (p=0.032)], PANSS-positive [OR=1.263 (p=0.013)] and CDSS [OR=1.198 (p=0.092)]. Males: only PANSS-positive [OR=1.123 (p=0.027)] and CDSS scores [OR=1.220 (p=0.005)] were associated with insomnia. Conclusions NLR represent an inflammatory marker of insomnia in Sch but only in female patients. Improving sleep quality in these patients could help to decrease their inflammatory response. Disclosure No significant relationships.
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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 | 0.000 | 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 | 0.000 |
| 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".