Bibliographic record
Abstract
Objective To study the diagnosis and treatment of perforated gastric cancer(PGC). Methods The clinical data of 43 patients with PGC proved pathologically were analysed retrospectively; Results The diagnosis rate preoperatively was 30.2%(13 cases), misdiagonsis rate 69.8% (30 cases, including 2 misdiagonsed intraoperatively); 21 underwent simple suture closure of the perforation, among whom, 4 died (19.1%), 8 had hematemesis and melanorragia (38.1%), 3 reperforation (1.4%) after operation; mean survival period was 5 months. 9 cases had palliative gastrectomy, 1 died, while 1 had hematemesis after operation; mean survival period 18 months. 13 cases had radical gastrectomy, none of them had postoperative complication or death, mean survival period 27 months, 3 surrived for 5 years(23.1%). 2 delyed diagnosis underwent simple suture closure at first, but had extensive metastasis at reoperation. Conclusions Comprehensive analysis of clcnical data made before operation and attenton paid to the signs of PGC during operation can enhance the diagnosis rate of PGC. Simple suture closure of the perforation has more complications, and the survival time is shorter . Once PGC gastric cancer is diagnosed , radical or palliative gastrectomy should be performed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".