Examining Time Intervals Between Diagnosis and Treatment in the Management of Patients With Limited Stage Small Cell Lung Cancer
Bibliographic record
Abstract
OBJECTIVE: To examine time intervals between diagnosis and treatment of limited stage small cell lung cancer (L-SCLC) and to evaluate its effect on clinical outcomes. MATERIALS AND METHODS: Data on 166 patients with L-SCLC referred to a regional cancer center between January 1991 and December 1999 were analyzed. The time intervals studied were defined as: interval A, first abnormal chest x-ray to pathologic diagnosis: interval B, diagnosis to first oncology consultation; interval C, oncology consultation to first day of thoracic radiotherapy (RT); interval D, oncology consultation to first day of chemotherapy; and interval E, first day of chemo to first day of RT. Cox proportional hazards models were used to examine associations between the time intervals and thoracic relapse (TR) and overall survival (OS) outcomes. Logistic regression analysis was used to model associations between time and complete response (CR) rates. RESULTS: The median time duration of intervals A to E were 20, 12, 63.5, 15, and 48 days, respectively. When time was analyzed as a continuous variable, no statistically significant association between the interval lengths and outcomes studied was observed. Dichotomizing each interval using the median value as cut-off revealed that interval A >20 days was significantly associated with improved CR (odds ratio = 3.573; P = 0.027) whereas interval B >12 days was associated with a trend toward lower CR (odds ratio = 0.348; P = 0.073). CONCLUSIONS: Short median times from first abnormal chest x-ray to diagnosis and from diagnosis to oncology consultation indicate that L-SCLC patients were diagnosed and referred promptly in the community setting. OS and TR appeared independent of the time intervals analyzed. Individual variations in disease presentation and tumor biology may explain the observed associations between early pathologic diagnosis and inferior CR rates.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".