Management evaluation of metastasis in the brain (MEMBRAIN)—a United Kingdom and Ireland prospective, multicenter observational study
Bibliographic record
Abstract
Abstract Background In recent years an increasing number of patients with cerebral metastasis (CM) have been referred to the neuro-oncology multidisciplinary team (NMDT). Our aim was to obtain a national picture of CM referrals to assess referral volume and quality and factors affecting NMDT decision making. Methods A prospective multicenter cohort study including all adult patients referred to NMDT with 1 or more CM was conducted. Data were collected in neurosurgical units from November 2017 to February 2018. Demographics, primary disease, KPS, imaging, and treatment recommendation were entered into an online database. Results A total of 1048 patients were analyzed from 24 neurosurgical units. Median age was 65 years (range, 21-93 years) with a median number of 3 referrals (range, 1-17 referrals) per NMDT. The most common primary malignancies were lung (36.5%, n = 383), breast (18.4%, n = 193), and melanoma (12.0%, n = 126). A total of 51.6% (n = 541) of the referrals were for a solitary metastasis and resulted in specialist intervention being offered in 67.5% (n = 365) of cases. A total of 38.2% (n = 186) of patients being referred with multiple CMs were offered specialist treatment. NMDT decision making was associated with number of CMs, age, KPS, primary disease status, and extent of extracranial disease (univariate logistic regression, P < .001) as well as sentinel location and tumor histology (P < .05). A delay in reaching an NMDT decision was identified in 18.6% (n = 195) of cases. Conclusions This study demonstrates a changing landscape of metastasis management in the United Kingdom and Ireland, including a trend away from adjuvant whole-brain radiotherapy and specialist intervention being offered to a significant proportion of patients with multiple CMs. Poor quality or incomplete referrals cause delay in NMDT decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".