Cost Analysis of Laparoscopic Low Anterior Resection vs. Transanal Endoscopic Microsurgery for Rectal Neoplasms
Bibliographic record
Abstract
Despite the increasing application of transanal endoscopic microsurgery (TEM) for rectal lesions, the cost of the equipment may play a role in a hospital’s hesitancy to invest in the platform. This study compares the cost of TEM to laparoscopic low anterior resection (LAR). Patients who underwent laparoscopic LAR (n = 24) for rectal neoplasm between 2006 and 2014 were case-matched based on sex, age, comorbidities, lesion size and location to patients who underwent TEM at a busy secondary care urban hospital. Procedure-related costs and costs associated with readmissions for complications and related subsequent surgeries in the first 3 years were calculated. There were 42 hospital admissions for 24 LAR patients, totalling 326 hospital days. For 24 TEM patients, there were 25 hospital admissions, totalling 56 hospital days. Subsequent operations for LAR patients included 2 washout and diverting ileostomies (8%), 2 adhesionolysis (8%), 4 ventral hernia repairs (16%) and 11 ileostomy reversals (46%). In the TEM group, there was one operation for recurrence (4%). The mean cost of LAR, including all related hospital costs in the subsequent 3 years, was CAD 14,851 (95% CI: CAD 10,124–19,579). The mean cost of TEM was CAD 2449 (95% CI: CAD 2133–2767; p < 0.0001), with a savings of CAD 12,402 per patient. TEM for rectal neoplasm is associated with significantly lower hospital costs, which far outweigh the costs of acquiring and maintaining the technology.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".