Does the interponat affect outcome after esophagectomy for cancer?<sup>*</sup>
Bibliographic record
Abstract
Clinical decision-making in esophageal cancer surgery is a process of balancing the risks of treatment against potential benefits, such as survival and quality of life. Various options are available for esophageal reconstruction. While these reconstructive options do not directly have an impact on cancer survival, they do affect operative morbidity and long-term quality of life. The affect of various interponats (reconstructive conduits) and routes of reconstruction on operative morbidity and foregut function is reviewed. Gastric interponats are preferred for esophageal reconstruction because of their reliable vascularity and the relative simplicity of the reconstructive operation. Colon interponats supposedly provide better long-term function as an esophageal substitute (unproven), but at the cost of increased operative complexity and morbidity. Colon interposition is therefore reserved for situations in which gastric transposition is not feasible. Both posterior and anterior mediastinal routes of gastric interponat reconstruction are acceptable (meta-analysis of randomized controlled trials). Posterior mediastinal reconstruction is usually preferred when a complete (R0) resection has been accomplished. Anterior mediastinal reconstruction may prevent secondary dysphagia after incomplete (R1, R2) resections.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".