Identifying a core symptom set triggering radiological and endoscopic investigations for suspected recurrent esophago-gastric cancer: a modified Delphi consensus process
Bibliographic record
Abstract
BACKGROUND: There is currently a lack of evidence-based guidelines regarding surveillance for recurrence after esophageal and gastric (OG) cancer surgical resection, and which symptoms should prompt endoscopic or radiological investigations for recurrence. The aim of this study was to develop a core symptom set using a modified Delphi consensus process that should guide clinicians to carry out investigations to look for suspected recurrent OG cancer in previously asymptomatic patients. METHODS: A web-based survey of 42 questions was sent to surgeons performing OG cancer resections at high volume centers. The first section evaluated the structure of follow-up and the second, determinants of follow-up. Two rounds of a modified Delphi consensus process and a further consensus workshop were used to determine symptoms warranting further investigations. Symptoms with a 75% consensus agreement as suggestive of recurrent cancer were included in the core symptom set. RESULTS: 27 surgeons completed the questionnaires. A total of 70.3% of centers reported standardized surveillance protocols, whereas 3.7% of surgeons did not undertake any surveillance in asymptomatic patients after OG cancer resection. In asymptomatic patients, 40.1% and 25.9% of centers performed routine imaging and endoscopy, respectively. The core set that reached consensus, consisted of eight symptoms that warranted further investigations included; dysphagia to solid food, dysphagia to liquids, vomiting, abdominal pain, chest pain, regurgitation of foods, unexpected weight loss and progressive hoarseness of voice. CONCLUSION: There is global variation in monitoring patients after OG cancer resection. Eight symptoms were identified by the consensus process as important in prompting radiological or endoscopic investigation for suspected recurrent malignancy. Further randomized controlled trials are necessary to link surveillance strategies to survival outcomes and evaluate prognostic value.
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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 | 0.244 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".