Quality of reporting of chemotherapy compliance in randomized controlled trials of breast cancer treatment
Bibliographic record
Abstract
OBJECTIVE: The Consolidated Standards of Reporting Trials statement requires detailed reporting of interventions for randomized controlled trials. We hypothesized that there was variable reporting of chemotherapy compliance in published randomized controlled trials in breast cancer, and therefore surveyed the literature to assess this parameter and determine the study characteristics associated with reporting quality. METHODS: Published Phase III randomized controlled trials (January 2005-December 2011; English language) evaluating chemotherapy in breast cancer were identified through a systematic literature search. Articles scored 1 point each for reporting of the four measures: number of chemotherapy cycles, dose modification, early treatment discontinuation and relative dose intensity. Logistic regression identified study characteristics associated with reporting quality score of ≥ 2. RESULTS: Of the 115 eligible randomized controlled trials, 79 (69%) were published in high-impact journals, 66 (57%) were published since 2008, 43 (37%) reported advanced-stage disease and 37 (32%) were industry sponsored. Relative dose intensity, number of cycles, dose modification and early treatment discontinuation were reported in 70 (61%), 53 (46%), 65 (57%) and 81 (70%) articles, respectively. Eighty-two (71%) articles showed a quality score of ≥ 2; 25 (22%) articles reported all four compliance measures. Articles published since 2008 (P = 0.035) and those reporting advanced-stage disease (P < 0.001) showed significantly higher quality of compliance. CONCLUSIONS: Our results demonstrate variable reporting of chemotherapy compliance in published randomized controlled trials with a modest improvement noted in recent years. Incorporating standards for reporting chemotherapy compliance in scientific guidelines or the journal peer review process may decrease the variability and improve the quality of reporting.
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 | 0.711 | 0.881 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".