Negative Predictive Value of Preoperative Computed Tomography in Determining Pathologic Local Invasion, Nodal Disease, and Abdominal Metastases in Gastric Cancer
Bibliographic record
Abstract
BACKGROUND: Before undergoing curative-intent resection of gastric adenocarcinoma (ga), most patients undergo abdominal computed tomography (ct) imaging to determine contraindications to resection (local invasion, distant metastases). However, the ability to detect contraindications is variable, and the literature is limited to single-institution studies. We sought to assess, on a population level, the clinical relevance of preoperative ct in evaluating the resectability of ga tumours in patients undergoing surgery. METHODS: In a provincial cancer registry, 2414 patients with ga diagnosed during 2005-2008 at 116 institutions were identified, and a primary chart review of radiology, operative, and pathology reports was performed for all patients. Preoperative abdominal ct reports were compared with intraoperative findings and final pathology reports (reference standard) to determine the negative predictive value (npv) of ct in assessing local invasion, nodal involvement, and intra-abdominal metastases. RESULTS: Among patients undergoing gastrectomy, the npv of ct imaging in detecting local invasion was 86.9% (n = 536). For nodal metastasis, the npv of ct was 43.3% (n = 450). Among patients undergoing surgical exploration, the npv of ct for intra-abdominal metastases was 52.3% (n = 407). CONCLUSIONS: Preoperative abdominal ct imaging reported as negative is most accurate in determining local invasion and least accurate in nodal assessment. The poor npv of ct should be taken into account when selecting patients for staging laparoscopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".