Discordant dry eye disease and chronic pain: A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: To evaluate the relative contributions of objective and subjective indicators of dry eye disease (DED) in individuals with chronic pain conditions compared with controls. METHODS: A systematic review and meta-analysis was conducted of studies that reported the signs and symptoms of DED and/or their prevalence in individuals with chronic pain compared with controls. International Association for the Study of Pain (IASP) International Classification of Diseases (ICD)-11 codes for chronic pain conditions were applied, and outcomes defined as DED signs and symptoms. A search strategy utilised the EMBASE, Web of Science, Cochrane Library and MEDLINE databases. Risk of bias assessment was performed with the Newcastle-Ottawa scale. Random effects meta-analysis calculated mean differences (MD) and odds ratios (OR), while subgroup analysis of different chronic pain conditions explored their relative association with the signs and symptoms of DED. Evidence certainty was evaluated using Grades of Recommendation, Assessment, Development, and Evaluation (GRADE). RESULTS: Fourteen observational studies comprising 3,281,882 individuals were included. Meta-analysis found high quality evidence that individuals with chronic pain were more likely to experience symptoms of DED than controls (OR = 3.51 [95 %CI: 3.45,3.57]). These symptoms were more severe (MD = 18.53 [95 %CI: 11.90, 25.15]) than controls with a clinically meaningful effect size. Individuals with chronic pain had more rapid tear film disruption (MD = -2.45 [95 %CI: -4.20, -0.70]) and reduced tear production (MD = -5.57 [95 %CI: -9.56, -1.57]) compared with controls (with moderate evidence quality). High quality evidence revealed individuals with chronic pain had lower basal tear production (anaesthetised) than controls (MD = -2.59 [95 %CI: -3.60, -1.58]). Tear film osmolarity showed no significant differences between the chronic pain and pain-free groups. Group differences for DED signs were not considered clinically meaningful. CONCLUSION: More severe, clinically meaningful symptoms of DED were reported in individuals with chronic pain than controls, however group differences for the signs of DED were typically of limited or questionable clinical relevance. This ocular phenotype where DED is felt more than it is seen in chronic pain may reflect underlying sensory hypersensitivity, shared by both conditions and contributing to their frequent comorbidity. Advancing understanding of this potential pathophysiological mechanism may guide clinical management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".