Variation in lung cancer diagnosis in England
Bibliographic record
Abstract
Objective There are several key investigations used in lung cancer diagnosis such as EBUS and PET scanning that some units have better access to than others. Part of the LungPath study aimed to access the degree of this inequality and measure how it affects lung cancer patients. Method Twenty willing NHS Trusts were randomly selected to participate in the LungPath study. Each centre agreed to submit data on each new lung cancer patient seen during the study period of six months. Data collected included anonymised pathology reports from all investigations performed along with clinical data and the dates of all radiological investigations performed. Each individual patient’s diagnostic pathway was mapped using the data collected. In addition, we collected information about typical waiting times for key investigations and whether these investigations were available on-site or at another institution. We analysed the patient pathways from the 20 different centres to see if there were differences in availability and use of Results We found there were dramatic differences in the availability and use of some key investigations, particularly EBUS and PET-scanning. We also found that the stage of the diagnostic pathway that these investigations were used varied and in many cases differed from national guidelines. Conclusion There are marked differences in the availability and use of EBUS and PET-scanning within different centres diagnosing lung cancer in England. There are implications for commissioners for more equal service provision and opportunities for education of clinicians to make best use of these resources.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".