Reforming support systems of newly diagnosed brain cancer patients: a systematic review
Bibliographic record
Abstract
PURPOSE: Despite the increasing incidence of currently incurable brain cancer, limited resources are placed in patients' support systems, with reactive utilisation late in the disease course, when physical and psychological symptoms have peaked. Based on patient-derived data and emphasis on service improvement, this review investigated the structure and efficacy of the support methods of newly diagnosed brain cancer patients in healthcare systems. METHODS: This systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols. Articles from PubMed, Embase, and CENTRAL databases were screened with six pre-established eligibility criteria, including assessment within 6 months from diagnosis of a primary malignant brain tumour. Risk of bias was evaluated using the Newcastle-Ottawa Scale and Critical Appraisal Skills Program (CASP) Qualitative Studies Checklist. RESULTS: Of 5057 original articles, 14 were eligible for qualitative synthesis. Four studies were cross-sectional and ten were descriptive. Information given to patients was evaluated in seven studies, communication with patients in nine, and patient participation in treatment decisions in eight. Risk of bias was low in ten studies, moderate in two, and high in two. CONCLUSIONS: Techniques promoting individualised care increased perceived support, despite poor patient-physician communication and complexity of the healthcare system. Extracted data across 14 included studies informed a set of guidelines and a four-step framework. These can help evaluate and reform healthcare services to better accommodate the supportive needs of this patient group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".