Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Many patients with lung cancer report delays in diagnosing their disease. This may contribute to advanced stage at diagnosis and poor long term survival. This study explores the delays experienced by patients referred to a regional cancer centre with lung cancer. METHODS: A prospective cohort of patients referred with newly diagnosed lung cancer were surveyed over a 3 month period to assess delays in diagnosis. Patients were asked when they first experienced symptoms, saw their doctor, what tests were done, when they saw a specialist and when they started treatment. Descriptive statistics were used to summarize the different time intervals. RESULTS: 56 of 73 patients consented (RR 77%). However only 52 patients (30M, 22F) were interviewed as 2 died before being interviewed and two could not be contacted. The mean age was 68yrs. Stage distribution was as follows (IB/IIA 10%, stage IIIA 20%, IIIB/IV 70%). Patients waited a median of 21 days (iqr 7-51d) before seeing a doctor and a further 22d (iqr 0-38d) to complete any investigations. The median time from presentation to specialist referral was 27d (iqr 12-49d) and a further 23.5d (iqr 10-56d) to complete investigations. The median wait to start treatment once patients were seen at the cancer centre was 10d (iqr 2-28d). The overall time from development of first symptoms to starting treatment was 138d (iqr 79-175d). CONCLUSIONS: Lung cancer patients experience substantial delays from development of symptoms to first initiating treatment. There is a need to promote awareness of lung cancer symptoms and develop and evaluate rapid assessment clinics for patients with suspected lung cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it