Low Grade Glioma: A Qualitative Study of the Wait and See Approach
Bibliographic record
Abstract
BACKGROUND: There is no consensus on the best management of adults with presumed low grade glioma (LGG). Studies have suggested uncertainty and anxiety associated with a wait and see approach contribute to reduced quality of life. This study aims to explore the impact of a diagnosis of LGG, to address concerns regarding the uncertainty of the diagnosis and the role of wait and see from the patient's perspective. METHODS: Qualitative research methodology was used. A semi-structured interview was conducted with 24 patients with imaging evidence of LGG but no prior intervention. All patients had been followed for at least one year prior to interview. Verbatim transcripts were subjected to thematic analysis. RESULTS: The median age of participants was 47 (range 21-82) and the median duration of follow-up 37 months (range 12-156 months). Fifty percent presented with seizures. Five overarching themes emerged from the data; 1) patients experience initial devastation followed by acceptance and low anxiety; 2) absence of symptoms mitigates anxiety concerning the possibility of progression; 3) patients would prefer to defer surgery until there is progression or a change in their quality of life; 4) anxiety is reduced by trust in the physician; 5) quality of life is not affected by the diagnosis, as fear of morbidity from intervention is greater than the fear of uncertainty. CONCLUSIONS: The wait and see approach does not contribute to anxiety or reduction in quality of life in patients with LGG.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".