Correlation between acute and chronic inflammatory states, a case control study
Bibliographic record
Abstract
Abstract Objective Suppression of efficient acute inflammation may be one of the mechanisms behind the onset of chronic low-grade inflammation. Efficient acute inflammatory response to pathogenic stimulus may not be possible in the presence of chronic inflammation. We investigated if a correlation exists between chronic and efficient acute inflammation. Methods Design Case control study. Setting Homeopathic medical practices in 4 countries Patients with definite improvement in chronic inflammatory conditions with at least 6 months of follow up were selected as cases. Age matched controls involved those who did not improve. Event of interest Occurrence of common acute infectious diseases with fever during the follow up period. Statistical analysis Odds Ratio of improvement in the chronic condition, with development of acute infectious diseases with fever was calculated. Graphs were plotted to study this correlation in individual cases. Results 20 cases and 20 age matched controls were selected. Average age was 28.4 and 27.9 years respectively. 18/20 cases and 4/20 controls developed common infectious diseases with fever during the follow up period. Odds Ratio of the chronic condition improving, with development of acute infectious diseases with fever was 36 (95 %, CI: 5.7973 to 223.5513), z statistic: 3.846 and significance level was p = 0.0001. Graphs of individual cases showed distinct patterns confirming the same. Conclusions In this case control study, appearance of common acute infectious diseases with fever during treatment was strongly associated with improvement in the chronic inflammatory conditions. Larger studies are needed to further establish the correlation. Supported by Nil
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".