Defining Prolonged Length of Stay (PLOS) Following Elective Laparoscopic Cholecystectomy and Derivation of a Preoperative Risk Score to Inform Resource Utilization, Risk Stratification, and Patient Consent
Bibliographic record
Abstract
OBJECTIVE: The present study defines prolonged length of stay (PLOS) following elective laparoscopic cholecystectomy (LC) and its relationship with perioperative morbidity. A preoperative risk tool to predict PLOS is derived to inform resource utilization, risk stratification and patient consent. BACKGROUND: Surgical candidates for elective LC are a heterogeneous group at risk of various perioperative adverse outcomes. Preoperative recognition of high-risk patients for PLOS has implications on feasibility for day surgery, resource utilization, preoperative risk stratification, and patient consent. METHODS: Data for all patients who underwent elective LC between January 2015 and January 2020 across 3 surgical centers (1 tertiary referral center and 2 satellite units) in 1 health board were collected retrospectively (n=2166). The optimal cut-off of PLOS as a proxy for operation-related adverse outcomes was found using receiver operating characteristic curves. Multivariate logistic regression was conducted on a derivation subcohort to derive a preoperative model predicting PLOS. Receiver operating characteristic curves were performed to validate the model. Patients were stratified by the risk tool and the risks of PLOS were determined. RESULTS: A LOS of ≥3 days following elective LC demonstrated the best diagnostic ability for operation-related adverse outcomes [area under curve (AUC)=0.87] and defined the PLOS cut-off. The rate of PLOS was 6.6% (144/2166), 86.1% of which had a perioperative adverse outcome. PLOS was strongly associated with all adverse outcomes (subtotal, conversion-to-open, intraoperative complications, postoperative complication/imaging/intervention) ( P <0.001). The preoperative model demonstrated good diagnostic ability for PLOS in the derivation (AUC=0.81) and validation cohorts (AUC=0.80) and stratified patients appropriately. CONCLUSIONS: Morbidity in PLOS patients is significant and pragmatic patient selection in accordance with the risk tool may help centers improve resource utilization, risk stratification, and their consent process. The risk tool may help select candidates for cholecystectomy in a strictly ambulatory/outpatient center.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".