Protocol for the CoNoR Study: A prospective multi-step study of the potential added benefit of two novel assessment tools in colorectal liver metastases technical resectability decision-making
Bibliographic record
Abstract
INTRODUCTION: Liver resection is the only curative treatment for colorectal liver metastases (CLM). Resectability decision-making is therefore a key determinant of outcomes. Wide variation has been demonstrated in resectability decision-making, despite the existence of criteria. This paper summarises a study protocol to evaluate the potential added value of two novel assessment tools in assessing CLM technical resectability: the Hepatica preoperative MR scan (MR-based volumetry, Couinaud segmentation, liver tissue characteristics and operative planning tool) and the LiMAx test (hepatic functional capacity). METHODS AND ANALYSIS: This study uses a systematic multistep approach, whereby three preparatory workstreams aid the design of the final international case-based scenario survey:Workstream 1: systematic literature review of published resectability criteria.Workstream 2: international hepatopancreatobiliary (HPB) interviews.Workstream 3: international HPB questionnaire.Workstream 4: international HPB case-based scenario survey.The primary outcome measures are change in resectability decision-making and change in planned operative strategy, resulting from the novel test results. Secondary outcome measures are variability in CLM resectability decision-making and opinions on the role for novel tools. ETHICS AND DISSEMINATION: The study protocol has been approved by a National Health Service Research Ethics Committee and registered with the Health Research Authority. Dissemination will be via international and national conferences. Manuscripts will be published. REGISTRATION DETAILS: The CoNoR Study is registered with ClinicalTrials.gov (registration number NCT04270851). The systematic review is registered on the PROSPERO database (registration number CRD42019136748).
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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 | 0.104 | 0.167 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.079 | 0.021 |
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".