Systematic review of active surveillance <i>versus</i> surgical management of asymptomatic small non-functioning pancreatic neuroendocrine neoplasms
Bibliographic record
Abstract
BACKGROUND: The incidence of asymptomatic, sporadic, small non-functioning pancreatic neuroendocrine neoplasms (NF-PNENs) has increased in recent decades. Conservative treatment has been advocated for these tumours. The aim of this study was systematically to evaluate the literature on active surveillance and to compare this with surgical management for asymptomatic sporadic small NF-PNENs. METHODS: PubMed, Embase and the Cochrane Library were searched systematically for studies that compared the active surveillance of asymptomatic, sporadic, small NF-PNENs with surgical management. PRISMA guidelines for systematic reviews were followed. RESULTS: After screening 3915 records, five retrospective studies with a total of 540 patients were included. Of these, 327 patients (60·6 per cent) underwent active surveillance and 213 (39·4 per cent) had surgery. There was wide variation in the tumour diameter threshold considered as inclusion criterion (2 cm to any size). The median length of follow-up ranged from 28 to 45 months. Measurable tumour growth was observed in 0-51·0 per cent of patients. Overall, 46 patients (14·1 per cent) underwent pancreatic resection after initial conservative treatment. In most patients the reason was an increase in tumour size (19 of 46). There were no disease-related deaths in the active surveillance group in any of the studies. CONCLUSION: This systematic review suggests that active surveillance of patients affected by sporadic, small, asymptomatic NF-PNENs may be a good alternative to surgical treatment.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".