Minimal Clinically Important Differences With the Outcomes of the App-Based Japanese Allergic Conjunctival Diseases Quality of Life Questionnaire: Cross-Sectional Observational Study
Bibliographic record
Abstract
BACKGROUND: Assessing changes in quality of life in patients with hay fever-related allergic conjunctivitis requires validated and clinically meaningful metrics. A minimal clinically important difference (MCID) that can be applied to assess Domain II of the Japanese Allergic Conjunctival Disease Quality of Life Questionnaire (JACQLQ) in a smartphone app setting has yet to be determined. OBJECTIVE: This cross-sectional observational study aimed to determine MCIDs for the app-based JACQLQ in assessing hay fever-related allergic conjunctivitis. METHODS: This study used data from a crowdsourced, cross-sectional, observational study conducted via the smartphone app "AllerSearch" between February 1, 2018, and May 1, 2020. Participants were recruited through digital media and social networking platforms and voluntarily provided electronic informed consent. Participants completed the JACQLQ, which includes items on daily activity and psychological well-being, as well as a visual analog scale to measure stress levels related to hay fever. Data were collected through the app, ensuring comprehensive user input. MCIDs were determined using both anchor- and distribution-based methods. The face scale of the JACQLQ Domain III and stress level scale for hay fever were used as anchors to estimate the MCID; ranges were derived from these MCID estimates. In the distribution-based method, MCIDs were calculated using half the SD and SE of the JACQLQ Domain II scores. SEs were derived from the intraclass correlation coefficient of an app-based JACQLQ test-retest reliability metric. RESULTS: A total of 17,597 individuals were identified, of which 15,749 individuals provided electronic consent. After excluding those with incomplete data, 7590 participants with hay fever were included in the study (mean age 35.3, SD 13.9 years; n=4331, 57.1% of women). MCID ranges calculated using the anchor-based method were 1.0-6.9, 1.2-5.6, and 2.1-12.6 for daily activity, psychological well-being, and total JACQLQ Domain II scores, respectively. Using the distribution-based method, the intraclass correlation coefficients were odds ratio (OR) 0.813 (95% CI 0.769-0.849) for daily activity, OR 0.791 (95% CI 0.743-0.832) for psychological well-being, and OR 0.841 (95% CI 0.791-0.864) for total JACQLQ Domain II scores. In addition, the distribution-based method resulted in 2 MCIDs based on half the SD and SE of measurement for daily activity (4.8 and 4.2), psychological well-being (3.4 and 3.1), and total JACQLQ Domain II (7.8 and 6.4) scores. The final suggested MCID ranges for daily activity, psychological well-being, and total JACQLQ Domain II scores were 4.2-6.0, 3.1-4.7, and 6.4-10.5, respectively. CONCLUSIONS: MCID ranges for the JACQLQ estimation could help to standardize the app-based quality of life assessment for patients with hay fever-related allergic conjunctivitis. These MCIDs enhanced the precision of remote symptom monitoring and facilitated timely, data-driven interventions, ultimately improving the overall management and outcomes of allergic conjunctivitis through mobile health platforms.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".