Diagnostic assessment of suspected cancer: Prospective cohort study of diagnostic delay intervals across three disease sites
Bibliographic record
Abstract
6029 Background: Diagnostic assessment of patients with suspected cancer can involve multiple points of delay. Methods: We conducted a cohort study of prospective eligible patients referred to a regional hospital in Ottawa, Ontario, Canada over a 12 month period (2004 to 2005) for diagnostic assessment of suspected colorectal (C), lung (L) or prostate (P) cancer. The study addressed three different disease sites, each with a different pre-existing model of care delivery, so that variability could be described. Measurement of diagnostic delay intervals included days to diagnosis (from referral to diagnosis communicated to the patient) and, for patients with cancer, to surgery and to oncology consultation. Differences between disease sites for diagnostic delay intervals, quality of life, and patient satisfaction were analyzed. Results: The proportion of patients who were diagnosed with cancer was: 6.8% (9/132) for C; 80.2% (81/101) for L; and 35% (41/116) for P. The mean [SD] days to diagnosis was significantly less for L (45 [32]) than for either C (81 [68]) for P (82 [43]), (p=.0001). It was also significantly less for patents diagnosed with cancer (57.8)[41.4]) than for those without cancer (79.3)[58.9]) (difference -21.5; 95% CI -33 to -9; p=.0003); a finding which held when C was analyzed separately (33.8 [48.9] vs 84.8 [67.7]; p=.04). L patients had worse physical and mental functioning as measured by the SF36 Physical and Mental Component Scores. Patient satisfaction did not differ across disease site or for patients with and without cancer. Conclusions: Most studies of diagnostic delay intervals use administrative data. This study is unique in prospectively measuring intervals that cannot be captured from administrative data and thus provides important benchmark measures. L patients were most likely to have cancer. Although C patients were unlikely to have cancer, the diagnostic interval was substantially shorter in those with cancer than in those without, suggesting that clinicians have an effective process for triaging patients referred for diagnostic assessment. No significant financial relationships to disclose.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".