Quality audit of colonoscopy reports amongst patients screened or surveilled for colorectal neoplasia
Bibliographic record
Abstract
AIM: To complete a �uality audit using recently pub�To complete a �uality audit using recently pub� complete a �uality audit using recently pub� lished criteria from the Quality Assurance Task Group of the National Colorectal Cancer �oundtable�� METHODS: Consecutive colonoscopy reports of patients at average/high risk screening, or with a prior colorectal neoplasia �C�N� by endoscopists who perform 11 000 procedures yearly, using a commercial computerized endoscopic report generator�� A separate institutional da� tabase providing pathological results�� �e�uired documen� tation included patient demographics, history, procedure indications, technical descriptions, colonoscopy findings, interventions, unplanned events, follow�up plans, and pathology results�� �eports abstraction employed a stan� dardized glossary with 10% independent data validation�� Sample size calculations determined the number of re� ports needed�� RESULTS: Two hundreds and fifty patients ������� �� Two hundreds and fifty patients ������� �� hundreds and fifty patients ������� �� and fifty patients ������� �� and fifty patients ������� �� fifty patients ������� �� patients ������� �� 10��5� years, female: ������%, average risk: ����5�%, per� years, female: ������%, average risk: ����5�%, per� years, female: ������%, average risk: ����5�%, per� : ������%, average risk: ����5�%, per� ������%, average risk: ����5�%, per� : ����5�%, per� ����5�%, per� sonal/family history of C�N: �����%/��0����%� were scoped : �����%/��0����%� were scoped �����%/��0����%� were scoped in June ��009 by � gastroenterologists and � surgeons �mean practice: 1���1 �� ���5� years��� �rocedural indica� : 1���1 �� ���5� years��� �rocedural indica� 1���1 �� ���5� years��� �rocedural indica� tion and informed consent were always documented�� 1�% provided a previous colonoscopy date �past polyp removal information in 25%, but insufficient in most to determine surveillance intervals appropriateness��� Most procedural indicators were recorded �exam date: 9����%, : 9����%, 9����%, medications�� ���2%, difficulty level�� ����%, prep �uality�� �� ���2%, difficulty level�� ����%, prep �uality�� ���2%, difficulty level�� ����%, prep �uality�� : 9����%, prep �uality: 9����%, prep �uality: : 99���%��� All reports noted extent of visualization �cecum: : 9����%, with landmarks noted in �����% � photodocu� mentation: ������%��� No procedural times were recorded�� : ������%��� No procedural times were recorded�� ������%��� No procedural times were recorded�� One hundred and eleven had polyps ������%� with ana� had polyps ������%� with ana� tomic location noted in 99��1%, size in �5����%, morphol� ogy in �������%; removal was by cold biopsy in ��5�����% �cold snare: 1�%, snare cautery: �1��5�%, unrecorded: ��0���%�, : 1�%, snare cautery: �1��5�%, unrecorded: ��0���%�, 1�%, snare cautery: �1��5�%, unrecorded: ��0���%�, : �1��5�%, unrecorded: ��0���%�, �1��5�%, unrecorded: ��0���%�, : ��0���%�, ��0���%�, , �����% were retrieved�� Adenomas were noted in ������% �advanced adenomas: ����%, cancer: 0���%� in this popu� : ����%, cancer: 0���%� in this popu� ����%, cancer: 0���%� in this popu� : 0���%� in this popu� 0���%� in this popu� lation with varying previous colonic investigations�� CONCLUSION: This audit reveals lacking reported ite� ms, justifying additional research to optimize �uality of reporting�� © ��01�� Baishideng�� All rights reserved��
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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 | 0.037 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".