An Online Preoperative Screening Tool to Optimize Care for Patients Undergoing Cancer Surgery: A Mixed-Method Study Protocol
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Despite surgery being the primary curative treatment for cancer, patients with compromised preoperative physical, nutritional, and psychological status are often at a higher risk for complications. While various screening tools exist to assess physical, nutritional, and psychological status, there is currently no standardised self-reporting tool, or established cut-off points for comprehensive risk assessment. This study aims to develop, validate, and implement an online self-reporting preoperative screening tool that identifies modifiable risk factors in cancer surgery patients. METHODS: This mixed-methods study consists of three distinct stages: (1) Development-(i) a scoping review to identify available physical, nutritional, and psychological screening tools; (ii) a Delphi study to gain consensus on the use of available screening tools; and (iii) a development of the online screening tool to determine patients at high risk of postoperative complications. (2) Testing-a prospective cohort study determining the correlation between at-risk patients and postoperative complications. (3) Implementation-the formulation of an implementation policy document considering feasibility. CONCLUSIONS: The timely identification of high-risk patients, based on their preoperative physical, nutritional, and psychological statuses, would enable referral to targeted interventions. The implementation of a preoperative online screening tool would streamline this identification process while minimising unwarranted variation in preoperative treatment optimisation. This systematic approach would not only support high-risk patients but also allow for more efficient provision of surgery to low-risk patients through effective risk stratification.
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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 | 0.067 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".