Frequency and Determining Factors of Empiric Chemotherapy Dose Reduction in Patients with Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Objectives: To determine the frequency and key factors regarding empiric chemotherapy dose reductions (ECDR) in nonsmall cell lung cancer (NSCLC) patients.Methods: This retrospective study involved the chart review of all histologically confirmed NSCLC patients receiving chemotherapy at the Odette Cancer Centre, Sunnybrook Health Sciences Complex, from 2013-2014.The frequency of ECDR and potential impacting factors were recorded and analyzed on SPSS v16.0.Results were expressed in percentages, P-values, and Cramer's V. Results: Our findings (N = 134) suggested patients with moderate kidney disease stages were statistically associated with ECDR (29% vs. 24% vs. 50% for stage I, II, and III respectively, p = 0.031, φ c = 0.235, df = 2).Patients aged 61 and above (39% vs. 14%, P = 0.001, φ c = 0.23, df = 1), polypharmacy of 4 or more medications (24% vs. 44%, p = 0.017, φ c = 0.21, df = 1), presence of kidney disease (43% vs. 24%, P = 0.024, φ c = 0.20, df = 1), and palliative intent (40% vs. 12%, p = 0.0027, φ c = 0.26, df = 1) showed statistically significant, but weak association with ECDR.Combinations (3 or more) of the impacting factors as mentioned above showed statistically significant association with ECDR (75% vs 42%, P = 0.00, φ c = 0.308).The degree of ECDR were positively correlated to patient age, moderate stage chronic kidney disease, and having combinations of impacting factors.Conclusions and Relevance: ECDR was common in the NSCLC patients (33%), particularly in the elderly, polypharmacy, palliative care and kidney disease population.Variances might exist among physicians, which might lead to clinically significant outcomes.Guidance and future studies for ECDR is crucial especially as Ontario's senior population grows.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".