Impact of a Single-Day Multidisciplinary Clinic on the Management of Patients with Liver Tumours
Bibliographic record
Abstract
PURPOSE: Multidisciplinary cancer clinics may improve patient care. We examined how a single-day multidisciplinary liver clinic (mdlc) affected care recommendations for patients compared with the recommendations provided before presentation to the mdlc. METHODS: We analyzed the demographic and clinicopathologic data of 343 patients assessed in the Johns Hopkins Liver Tumor Center from 2009 to 2012, comparing imaging and pathology interpretation, diagnosis, and management plan between the outside provider (osp) and the mdlc. RESULTS: Most patients were white (n = 259, 76%); median age was 60 years; and 146 were women (43%). Outside providers referred 182 patients (53%); the rest were self-referred. Patients travelled median of 83.4 miles (interquartile range: 42.7-247 miles). Most had already undergone imaging (n = 338, 99%) and biopsy (n = 194, 57%) at the osp, and a formal management plan had been formulated for about half (n = 168, 49%). Alterations in the interpretation of imaging occurred for 49 patients (18%) and of biopsy for 14 patients (10%). Referral to the mdlc resulted in a change of diagnosis in 26 patients (8%), of management plan in 70 patients (42%), and of tumour resectability in 7 patients (5%). Roughly half the patients (n = 174, 51%) returned for a follow-up, and 154 of the returnees (89%) received treatment, primarily intraarterial therapy (n = 88, 57%), systemic chemotherapy (n = 60, 39%), or liver resection (n = 32, 21%). Enrollment in a clinical trial was proposed to 34 patients (10%), and 21 of the 34 (62%) were accrued. CONCLUSIONS: Patient assessment by our multidisciplinary liver clinic had a significant impact on management, resulting in alterations to imaging and pathology interpretation, diagnosis, and management plan. The mdlc is an effective and convenient means of delivering expert opinion about the diagnosis and management of liver tumours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".