Reference value models for predicting preoperative six-minute walk test in patients scheduled for abdominal and pelvic cancer surgery
Bibliographic record
Abstract
Preoperative assessment of functional capacity with the six-minute walk test (6MWT) allows for estimation of surgical risk and targeted triage to prehabilitation services. Patient with abdominal and pelvic cancers have worse preoperative function compared with the general population. However, six-minute walk distance (6MWD) reference values from cancer patients are unknown, which limits the interpretation of 6MWT in this population. This study aimed to establish an explanatory reference value model for preoperative 6MWD in patients with abdominal or pelvic cancers undergoing elective surgery. Adult patients undergoing surgery for abdominal or pelvic cancers at major international hospitals were included. The 6MWT was assessed before surgery using a standardised protocol. Anthropometric data including age, sex, height, weight and body mass index (BMI) were collected and included in multiple linear regression analysis to model preoperative 6MWD. A total of 742 patients were included. Age, height and BMI were correlated with 6MWD. Six regression models were estimated, including two from the entire cohort, two from the subset of males and two from the subset of females. A sex-neutral model was the most representative, explaining 15% of the variance in 6MWD (6MWD = 761.00–3.00 * Age (years) –2.86 * BMI (kg/m 2 ) – 48.09 * Sex (M1, F2)). The explored regression models, using anthropometric variables, poorly explained the variance between measured and modelled 6MWD, which suggests that these models have no clinical utility in the cancer population. Consideration of additional, non-anthropometric variables may improve regression modelling of preoperative 6MWD in patients in abdominal and pelvic cancers.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".