Chemotherapy Uptake and Wait Times in Early-Stage Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Treatment uptake and elapsed times along the care path have emerged as potential quality indicators for cancer care delivery. This retrospective study examined changes in adjuvant chemotherapy uptake and elapsed times along the care path for patients in 2005 and in 2007 who had early-stage non-small-cell lung cancer (nsclc) and who underwent curative-intent surgery in Nova Scotia, Canada. METHODS: All patients who underwent curative-intent surgery for stages i-iii nsclc in the two years of interest were included. Logistic regression and general linear models were used to examine factors associated with chemotherapy uptake patterns and, at various resolutions (low, intermediate, high), elapsed times between all care events in the care path. RESULTS: In the 223 patients who underwent curative-intent surgery (108 in 2005, 115 in 2007), several factors were associated with uptake patterns and elapsed times. Cohort year (2007 vs. 2005) was not associated with referral to medical oncology [odds ratio (or): 1.05; 95% confidence interval (ci): 0.51 to 2.15; p = 0.905], but it was associated with less treatment after referral (or: 0.34; 95% ci: 0.11 to 1.00; p = 0.057) and less overall uptake (or: 0.35; 95% ci: 0.13 to 0.95; p = 0.040). Patients were referred sooner to medical oncology in 2007 than in 2005 (21 days vs. 35 days, p = 0.008), but experienced longer waits between consultation and chemotherapy delivery (18 days vs. 7 days, p = 0.001). CONCLUSIONS: Significant differences were observed in care patterns over time. Frequent monitoring of care patterns at high resolution may optimize insights into emerging trends within cancer care systems.
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.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".