A Retrospective Analysis of the Diagnosis of Gastroenteropancreatic Neuroendocrine Tumors at The Ottawa Hospital Cancer Center over the Last Decade Including COVID-19 Pandemic Period
Bibliographic record
Abstract
INTRODUCTION: The incidence of neuroendocrine tumors (NETs) is rising. Our objective was to assess trends in gastroenteropancreatic (GEP)-NETs diagnosis (June 2010 to June 2021) at TOHCC and to explore whether early COVID-19 pandemic data impacted these trends. METHODS: This was a single-center retrospective chart review of data collected from June 2010 to June 2021. We searched all databases, including OACIS/EPIC, PACS, and OPIS and found 647 GEP-NET patients. Descriptive analyses were performed using frequencies and related percentages. RESULTS: Of 647 patients with GEP-NETs, the small bowel was the most common primary location (n = 210, 32.4%), followed by the pancreas (n = 118, 18.2%), and unknown primary location (n = 99, 15.3%). Most of the cases were classified as metastatic or locally advanced at the initial presentation. There has been no significant variation in the frequency distribution of these cases over the last decade. Stages 1 and 2 were found in 158 cases (23.8%), and lower gastrointestinal (GI) tumors were the most common disease among them (n = 88, 55.7%). There were 5 lower GI cases in 2010-2011 and average number per registration year was 5.5 until 2016-2017, after which time the number of cases increased to 10, 15, 11, and 13 during the last 4 years. Regarding early-stage pancreatic and upper GI NETs, the total number of cases was 52 (32.9%) and 18 (11.4%), respectively. The average number of cases per registration year for pancreatic tumors was 4.7, while that for upper GI tumors was 1.6 over the last decade. DISCUSSION: At our center, most GEP-NETs presented in an advanced setting. Small bowel is the most common location overall. The incidence of early-stage disease has increased. Disease detection for all GEP-NETs was consistent throughout the last decade, except for the lower GI cases that have increased since mid-2017, perhaps reflecting the adoption of Ontario FIT testing. Despite endoscopy closures and disruption of some diagnostic services during the pandemic, cases of GEP-NETs for all stages did not decrease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".