The diagnostic pathway for patients with interstitial lung disease: a mixed-methods study of patients and physicians
Bibliographic record
Abstract
OBJECTIVES: The diagnostic process for patients with interstitial lung diseases (ILD) remains complex. The aim of this study was to characterise the diagnostic care pathway and identify barriers and potential solutions to access a timely and accurate ILD diagnosis. DESIGN: This mixed-method study was comprised of a quantitative chart review, patient and physician surveys and focus groups. RESULTS: Chart review was completed for 97 patients. Median time from symptom onset to ILD diagnosis was 12.0 (IQR 20.5) months, with 46% diagnosed within 1 year. Time from first computed tomography (CT) scan to respirology referral was 2.4 (IQR 21.2) months. Referrals with a prior CT were triaged sooner than referrals without (1.7±1.6 months vs 3.9±3.3 months, p=0.013, 95% CI 0.48 to 2.94). On patient surveys (n=70), 51% felt that their lung disease was not recognised early enough. Commonly reported challenges to timely diagnosis included delayed presentation to primary care, initial misdiagnoses and long wait-times for specialists. Forty-five per cent of physicians (n=20) identified diagnostic delays, attributed to delayed presentations to primary care (58%), initial misdiagnoses (67%) and delayed chest imaging (75%). Themes from patient and respirologist focus groups included patient-related, healthcare provider-related and system-related factors leading to delays in diagnosis. CONCLUSIONS: This mixed-methods study identified patient and system-related factors that contribute to diagnostic delays for patients with ILD, with most delays occurring prior to respirology referral. ILD awareness and education, earlier presentation to primary care, expedited access to chest imaging and earlier referral to respirology may expedite diagnosis.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".