Indicators of Functional Status for Primary Malignant Brain Tumour Patients
Bibliographic record
Abstract
BACKGROUND: We compared the functional status and survival time of patients with malignant gliomas. METHODS: This retrospective review included 143 patients diagnosed with malignant gliomas. Patients were grouped according to histopathological diagnosis. To measure functional status, patients were assigned a Karnofksy performance status (KPS) score at the time of presentation and at one, three, six, nine, 12 months and yearly intervals thereafter. Data were analyzed using descriptive methods as well as Kruskal-Wallis tests, Chi-square tests, Log-Rank tests and Cox's proportional hazards modeling. RESULTS: Eighty-four patients were male. The median age of patients was 63 years. One hundred and seven patients had a histopathological diagnosis of glioblastoma multiforme, 23 of anaplastic astrocytoma and 13 of anaplastic oligodendroglioma. Twenty-nine patients received aggressive multimodal treatment, 83 received intermediate treatment and the remaining 31 patients received conservative therapy. Significant treatment complications occurred in 33% of patients including four post-operative deaths. The anaplastic oligodendroglioma group had lower mortality and maintained better KPS scores over time, as did patients receiving full treatment. The most significant prognostic factors for functional status included age, pretreatment KPS, and type of treatment received. The most significant factors associated with time until death included age, severity of comorbidities, pretreatment KPS, presence of confusion, histopathological diagnosis and type of treatment received. CONCLUSION: In patients with malignant gliomas, younger age, better functional status at presentation and aggressive multimodal treatment were associated with improved longer-term functional status and survival. Confirmation of the effect of multimodal treatment on patient functional status would require a randomised controlled clinical trial.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".