Challenges in the Understanding of Oligometastatic Disease in Clinical Practice
Bibliographic record
Abstract
There is little data describing oligometastatic disease (OMD) and decision-making. We sought to understand the knowledge gaps and challenges in deciphering and delivering treatments. MATERIALS AND METHODS: This is a quality improvement (QI) study conducted via an anonymous survey. Three different clinical scenarios of OMD (oligo-recurrence disease, synchronous de-novo OMD, and oligo-progressive disease) were presented to assess participants' comprehension. A qualitative approach was used, involving four open-ended questions. Summary statistics and descriptive analysis were utilized to describe survey answers. RESULTS: The survey was answered by 70 clinicians, 56% (n=39) medical oncologists, 24% (n=17) radiation oncologists, 7% (n=5) surgeons, and the remaining 13% (n=9) from anatomical pathology, radiology, and palliative care. The three clinical cases were correctly identified as oligo-recurrence, de-novo, and oligo-progression disease in 63% (n=44), 94% (n=66), and 76% (n=53) of responses, respectively. Additionally, for each case, the majority of respondents indicated that they would offer local treatment (n=59, 84%; n=57, 81%; n=55, 79%, respectively). Seventy-nine percent (n=49) perceived differences between each modality of local therapies. Physicians perceived challenges including the lack of prospective trial data and unclear approach to OMD. An important determinant in deciding whether patients may benefit from treatment was tumor histology. CONCLUSION: The term OMD involves certain difficulties in definition and management. Positive and negative trials have further added uncertainty regarding who would best benefit from local treatment. The discordance in outcome expectations from physicians and patients will need to be addressed to ensure that patient's goals of care are met.
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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 | 0.075 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".