Identifying the Initiation of a New Line of Therapy for Metastatic Lung, Breast, and Colorectal Cancer in Real‐World Data: A Scoping Review
Bibliographic record
Abstract
PURPOSE: This study aimed to identify and synthesize published algorithms for identifying the initiation of a new line of therapy (LOT) for metastatic lung, breast, and colorectal cancer in real-world data (RWD). METHODS: We conducted a scoping review of published, English-language studies describing algorithms for identifying any LOTs with systemic anti-cancer therapy (SACT) for either non-metastatic or metastatic lung, breast, or colorectal cancer in RWD between January 1, 2014, and April 29, 2024. Dual reviewers independently screened titles, abstracts, and full-text articles, with disagreements resolved by a third reviewer. Data were extracted, categorized, synthesized, and summarized in narrative and tabular formats. RESULTS: The review identified 25 studies, mainly (64%) from the United States. Electronic health/medical records (EHRs) were the most frequently utilized (72%) RWD source. Twenty-four studies (96%) described RWD algorithms for identifying the initiation of a new LOT for metastatic lung, breast, or colorectal cancer. In 23 studies, algorithms required observing a new, adjuvant, SACT after an "incident" metastatic diagnosis code, which had been preceded by a metastasis-free lookback period of varied duration. Three studies' algorithms required observation of the completion of non-metastatic LOTs before initiation of a new LOT for metastatic cancer. Three studies validated their algorithms. CONCLUSIONS: Different algorithms are being used to identify LOT initiation for metastatic cancer. Most algorithms require an incident diagnosis of metastasis before considering subsequent SACT as newly initiated LOT for metastatic cancer. However, definitions of metastasis onset and gap duration to therapy initiation vary.
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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 | 0.106 | 0.362 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".