Pain is a common problem in patients with ILD
Bibliographic record
Abstract
Abstract Background Less is known about the prevalence and characteristics of pain in interstitial lung disease (ILD) patients. To determine the characteristics of pain in ILD patients.Methods Participants with ILD and age, gender-matched, healthy controls completed short form McGill Pain Questionnaire (SF-MPQ) and part of the Brief Pain Inventory short form(BPI) to elicit pain characteristics. ILD patients also had assessments of pulmonary function test, six minutes walking test (6MWT), modified medical research council dyspnea scale (mMRC) for state of the illness and measured health-related quality of life(HRQoL) by short form-36(SF-36)and psychological associations by hospital anxiety and depression scale(HADS).Results A total of 63 participants with ILD and 63 healthy controls(HC) were recruited in our study. The prevalence of pain was 61.9% in ILDs versus 25.3% in HC (p = 0.005) and the median score of pain rank index (PRI) in ILDs was higher than in HC (P = 0.014). Chest(46.1%) accounted for the highest of overall pain locations in participants with ILD. Associated clinical factors for pain intensity in ILD patients included younger age (< 60 years), exposure history of ILD risk factors, longer distance of 6MWD(≥ 250 m), higher mMRC score(2–4) and lower DLCo, % predicted(≤ 45%). ILD patients with pain are more likely to suffer impaired HRQoL(P = 0.0014) and psychological problems(P = 0.0017,P = 0.044).Conclusion Pain is common in those with ILD and the pain intensity is associated with age, exposure history, 6MWD, mMRC score and DLCo, % predicted. ILD patients with pain have more possible to suffer depression, anxiety and impaired HRQoL.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".