Impact of an Accelerated Diagnostic Assessment Program on the Timeliness of Cancer Diagnosis and Treatment
Bibliographic record
Abstract
PURPOSE: The Accelerated Diagnostic Assessment Program (ADAP) manages patients with imaging abnormalities, with or without concomitant symptoms, where cancer is suspected. The ADAP is offered to primary care practitioners and emergency departments with cases triaged by a medical oncologist. METHODS: We performed a retrospective patient chart review of electronic medical records from January 2019 until June 2021 to validate the program. We collected information on the referral pathways, patient demographics, wait-times, and diagnostic results. The control group consisted of outpatients who were referred for biopsy over a 1-year period outside the ADAP stream. Statistical analyses were performed using IBM SPSS software. RESULTS: Of the 97 patients included, 54% were female, with ages ranging from 18 to 96 years. Twenty-nine percent (n = 20) of the malignant cases were incidental findings. Most patients referred to the ADAP were diagnosed with a malignancy (71%; n = 69), comprising hematologic (45%; n = 31), GI (26%; n = 18), or other cancers (29%; n = 20). The ADAP had decreased wait-times from referral to biopsy collection (17.6 days ± 10.7 [standard deviation (SD)]; n = 43) when compared with the control group (41.2 days ± 40.0 [SD]; n = 67; P < .001). ADAP patients with malignancies saw a treating specialist 7.6 ± 7.6 days [SD] after their follow-up appointment at the ADAP. CONCLUSION: The ADAP accelerated time to biopsy in a statistically significant manner when compared with age-, referring physician–, and biopsy site–matched controls. It also outperformed national and provincial standards, suggesting that its model addresses a gap in care by providing an underserved population timely access to diagnosis and treatment.
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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 | 0.001 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".