A successful clinical pilot registry of four radiation oncology practices in Africa and Ontario.
Bibliographic record
Abstract
OBJECTIVE: Radiation Oncology practices can exhibit heterogeneities between and sometimes within institutions. Clinical registries with scope and detail could quantify consistency and distinctives that justify difference. Retrospective, isolated clinical audits are problematic, typically because not all data are captured in charts, while useful prospective clinical registries will have to be practical, efficient and accurate. We tested feasibility of a clinical registry at a critical time-point in the patient's clinical trajectory when treating physicians could have requisite data. DESIGN: This was a prospective and non-randomized observational study. Four centres used a 1-page form to acquire data during a 4-month period. Patients had curative breast, rectum or prostate cancers, or were palliative. Objectives were to demonstrate form completion and to delineate patterns of disease presentation and clinical practice. RESULTS: The 107 cases had 99% complete data, internally consistent within cases and centres. Similar practices were seen for 22 cases with curative rectal and prostate cancer, and 34 palliative cases, but of the 51 curative breast cancer cases those in Africa were with greater Stage, underwent more extensive surgery, were less likely to receive shorter radiation schedules, and were less exposed to Taxane-based chemotherapy regimens. CONCLUSIONS: This study demonstrates the feasibility for a simple clinical registry requiring minimal effort by participants. A real-time pan-African registry, operating continually or in regular waves, could provide important knowledge at little cost.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".