S5 Real-world evidence on the journey of lung cancer patients in England: delays in diagnosis and treatment
Bibliographic record
Abstract
Introduction Lung Cancer (LC) is the most frequently diagnosed cancer worldwide. Timeliness in diagnosis is crucial, as delays contribute to worsened survival. Diagnostic delays can lead to unplanned healthcare utilisation, including A&E visits. In the UK, the National Lung Cancer Audit evaluates quality metrics. To improve LC care, this study was conducted to understand patterns in patients not meeting national goals in real-world settings. Methods Adult patients (≥ 18 years) diagnosed with LC (ICD-10: C34) between 1 April 2018 and 31 March 2019 were included. Linked data from Hospital Episode Statistics (HES) and the Diagnostic Imaging Database (DID) were used. Patients were excluded if they had another primary cancer or no records in DID prior to diagnosis. Records of chest imaging in the six months before diagnosis were extracted using OPCS-4 codes in HES and SNOMED, modality, or NICIP codes in DID. Presence of a code for imaging and a code for body part (i.e., chest) were both required. Treatments received within 12 months after diagnosis were analysed. Intervals were reported as median days and upper quartiles (75th percentile). Proportions of patients visiting A&E were reported. Results A total of 21,052 patients diagnosed with LC during the study period were included. Median time from first chest imaging to diagnosis was 56 days, with the upper quartile of 113 days. Almost half (46%) of patients had more than one chest X-ray and 17% had more than one CT scan before diagnosis. Median time from first chest imaging to treatment was 84 days, with the upper quartile of 136 days. A third (31%) were diagnosed via emergency presentation and more than half (53%) visited A&E between first imaging to treatment. Patients whose diagnosis took longer (> 56 days) had on average two A&E visits before treatment, compared to one visit in those diagnosed sooner (≤ 56 days). Conclusion Timeliness of LC care remains an ongoing concern and this research provides real-world evidence to understand disparities in diagnostic pathways. Prolonged time to diagnosis and treatment can lead to increased healthcare utilisation with repeated diagnostic imaging and unplanned A&E visits.
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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 | 0.005 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.002 |
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".