Evaluation of Diagnostic Assessment Units in Oncology: A Systematic Review
Bibliographic record
Abstract
PURPOSE: This systematic review was undertaken to identify clinical and economic evaluations of diagnostic assessment units for cancer; summarize measures used to evaluate such programs; and discuss the strengths and weaknesses of these evaluations. METHODS: The review was conducted to identify randomized controlled trials, case control studies, and prospective or retrospective cohort studies examining the outcomes of diagnostic centers for patients with a presumptive diagnosis of breast, colorectal, lung, head and neck, or prostate cancer. Data on methodology and study results were tabulated. RESULTS: Twenty articles were eligible for review. Eleven studies examined outcomes associated with breast cancer assessment units: six with head and neck assessment units and three with colorectal assessment units. No studies were found that examined one-stop diagnostic assessment centers for lung cancer or prostate cancer. Seventeen studies were case series, one was a case-control study, and two were randomized controlled trials. No thorough economic analyses have been undertaken. There were no studies that based their assessment on measures suggested by a conceptual framework or validated model of diagnostic care. Few studies explicitly based their investigations on established quality indicators or clinical practice guideline recommendations. Diagnostic assessment centers appear to decrease the time to arrive at a diagnosis, which in turn appears to decrease patient anxiety and increase patient satisfaction. CONCLUSION: A comprehensive understanding of the benefit of diagnostic assessment centers can only be determined if such services are developed for a variety of disease sites and more rigorous evaluations are carried out to assess their benefit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".