Monitoring postcolonoscopy colorectal cancers: dangerous crossroads?
Bibliographic record
Abstract
Quality indicators for colonoscopy should be designed to render the outcomes of healthcare services measurable and transparent for both patients and physicians. For example, as the goal of colonoscopy is to prevent and detect colorectal cancer (CRC), improved quality should minimise the postcolonoscopy colorectal cancer (PCCRC) rate. In fact, most other indicators of procedural quality, such as adenoma detection rate and caecal intubation rate, owe their validation to a correlation with PCCRC rate. In an ideal world, rigorous monitoring of PCCRC rates can be used for benchmarking at multiple levels (regional, national, international) and would be a key driver of colonoscopy quality improvement within and outside screening programmes. It is therefore crucial to use a common language and common methodology when measuring, monitoring and reporting PCCRC. The process of benchmarking is neither quick nor simple. It starts with the implementation of a uniform terminology for a PCCRC. The term ‘PCCRC’ refers to colonoscopy in general, performed for screening, surveillance or symptoms, whereas the term ‘interval CRC’ refers to screening and colonoscopy surveillance, when a follow-up time interval is specified (intention-to-screen).1 The next key issue is what and how to monitor for calculating PCCRC rates. Several caveats should be recalled, foremost of which are the lack of complete clinical information, hurdles in crosslinking a cancer registry to colonoscopy databases and ambiguity on how to calculate rates. Such factors hinder meaningful interpretation of PCCRC rates and defining of quality standards, as shown by Morris et …
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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 | 0.111 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".