External Validation of the LabBM Score in Patients With Brain Metastases
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to validate the prognostic impact of the recently introduced three-tiered LabBM score in patients with brain metastases. In contrast to the previous development and validation cohorts, the present cohort did not include patients treated with primary surgery and/or radiosurgery. The score is based on hemoglobin, platelet counts, albumin, C-reactive protein and lactate dehydrogenase. METHODS: This was a retrospective single institution analysis. Overall, 167 patients managed with first-line whole-brain radiotherapy (WBRT) were identified from a prospectively maintained database. RESULTS: The LabBM score significantly predicted overall survival (median 4.0, 2.9 and 1.5 months, respectively). CONCLUSIONS: The LabBM score is also valid in a patient population that differs from the previously studied cohorts, that is patients who were judged to be better candidates for WBRT than surgery or radiosurgery. As these patients in general represent a less favorable subset, their median survival was shorter than reported in the development cohort (11, 7 and 3 months, respectively). Future studies should examine whether or not combinations of the LabBM and other scores, for example, lung-molGPA and melanoma-molGPA, improve the clinical value of single scores.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".