HOUT-13. PATIENT REPORTED OUTCOME MEASURES IN BRAIN METASTASES PATIENTS – A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Brain metastases (BMs) represent an important health care problem, with increasing incidence rates over time. Due to the heterogeneity of patient and tumor characteristics, it is hard to find consensus on optimal treatment strategy. Especially in palliative care, it is important to measure treatment effects on quality of life (QOL) from the patients’ perspective. Patient reported outcome measures (PROMs) can facilitate optimal treatment choice and even improve outcome in BMs patients. The aim of this review was to systematically analyze implementation and validity of different PROMs in BMs patients. PubMed, Embase, and Cochrane databases were systematically searched for studies on PROMs in patients with BMs. Study selection, data extraction, and quality assessment were performed independently by two investigators. Quality of PRO reporting was determined using the ISOQOL-recommended PRO reporting standards and each questionnaire was assessed using the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) grading criteria. In total, 53 studies were included, comprising 5358 patients with primary tumor types: lung (61%), breast (16%), gastrointestinal (4%), renal (3%), melanoma (2%), urogenital (2%), other (8%), and unknown (4%). Nine different PROMs were applied, ranging from general cancer (FACT-G, EORTC-QLQ-C15-PAL, ESAS, SF-36, McGill QOL, EuroQol-5D) to brain specific (FACT-Br, BASIQ, EORTC-QLQ-BN20) questionnaires. Half of the general cancer questionnaires (n=3) were not validated among BM patients. According to a predefined cut-off point in line with previous work, 22 studies had insufficient quality of PRO reporting. A variety of PROMs are used in BM patients, including questionnaires intended for general cancer and general patient populations, which have not been validated in patients with BMs. Given the unique clinical considerations in patients with brain metastases, our results indicate the need for a standardized and validated questionnaire with high level of reporting for BM patients.
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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 | 0.020 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".