A57 ROLE OF DYNAMIC MRI DEFECOGRAPHY IN IDENTIFICATION OF PELVIC FLOOR DYSFUNCTION: A TERTIARY CENTRE EXPERIENCE.
Bibliographic record
Abstract
Dynamic MRI defecography is a relatively new imaging protocol which can be extremely useful in identification of anatomic and functional pelvic floor dysfunction such as organ prolapse, anismus and fecal incontinence. The aim of the study is to assess for causes of Pelvic floor dysfunction on MRI and further characterize the findings based on functional or anatomical causes. Retrospective case series of all patients having from January 2017 to August 2018 at a tertiary care hospital (South Health Campus, Calgary, AB). After injecting rectal ultrasound gel the study was performed in resting, squeezing and defecation sequences. At least four defecation sequences were obtained to assess for complete evacuation of rectal vault. The images were then carefully reviewed to identify for descent of urinary bladder (cystocele), uterus, enterocele and rectum. The degree of prolapse was then measured and graded according to the set guidelines in radiology literature. Anismus was identified if the patient was unable to evacuate the rectal gel in four separate sequences of defecation. A total of 66 patients underwent MRI Defecography. Majority of the patients referred for MRI had clinical history of constipation and to assess for compartment prolapse.The most common finding was excessive compartmental descent in 77% of patients and anismus in 38% (Table). Two patients had normal study and two patients had tumours identified as the cause for their symptoms. Most patients were referred by gastroenterologists and very few (5%) had anorectal manometry. Dynamic MR defecography is a novel tool for identification of both anatomic and functional pelvic floor abnormalities. The information it provides may allow for effective management (eg physiotherapy and/or biofeedback for anismus, and surgical correction for significant prolapse). Complete evaluation of the pelvis can also yield additional information such as tumors or other miscellaneous findings. However, while sensitivity appears excellent, further study is required to ascertain specificity of MR diagnosis of anismus due to patients who may have difficulty defecating due to the non-physiologic aspect of supine defecation. Given that MR scanners are much more common than anorectal manometry labs, wider adoption of MR defecography may improve diagnostic capability for pelvic floor dysfunction, which is commonly under-diagnosed. RESULTS None
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".