NANO-LM: An updated scorecard for the clinical assessment of patients with leptomeningeal metastases
Bibliographic record
Abstract
BACKGROUND: There are no validated tools for the clinical neurological assessment of patients with leptomeningeal metastases (LM). However, clinical examination during the course of the disease guides medical management and is part of response assessment in clinical trials. Because neuroimaging may not always be obtained owing to rapid clinical deterioration, clinical neurological assessment of LM is essential, and standardization is important to minimize rater disagreement. METHODS: The Response Assessment in Neuro-oncology-LM group launched a 2-step process, aiming at improving and standardizing the clinical assessment of patients with LM. We report here on the first step the establishment of a consensus scorecard. The task force had 9 virtual meetings to define general recommendations on neurological assessment and selected domains of interest that should be tested. RESULTS: Fourteen domains of neurological symptoms and signs were selected: level of consciousness, cognition, nausea and vomiting, vision, eye movement, facial strength, hearing, swallowing, speech, limb strength, limb ataxia, walking, and bladder bowel functions. For each item, a clear instruction on how to perform the assessment is provided with scoring criteria between 0 and 2. The general clinical status of the patient and use of steroids, pain medications, and antiemetics should be documented. Neurological sequelae from previous brain metastases or cancer treatment should be rated at the baseline evaluation; it should be specified when symptoms or signs may be related to a condition other than LM. DISCUSSION: A revised Neurological Assessment in Neuro-Oncology-LM consensus scorecard for clinical assessment has been established. An international prospective validation study of the proposal is currently ongoing (NCT06417710).
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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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".