Cancer care coordination systematic review and meta-analysis: Twenty-two years of empirical studies.
Bibliographic record
Abstract
6536 Background: To our knowledge, no systematic review of empirical papers describing cancer care coordination interventions has yet been conducted. The aim of this presentation is to describe the methods and findings from a systematic review and meta-analysis of all empirical papers describing cancer care coordination published between 1990-2012. Methods: Of 1241 abstracts collected from a search of PubMed and EMBASE, 108 studies were retrieved and reviewed; 49 were included in the systematic review. Each study had US or Canadian adult or child participants; each paper had comparison or control groups, measures, samples, and/or interventions. Two researchers independently applied a standardized search strategy, coding scheme, and on-line coding program to each study. Eight RCT’s met additional criteria for meta-analysis; a random effects estimation model was used for data analysis. Results: Among the 49 articles included in our systematic review, those that included implicit or explicit definitions of cancer care coordination described four components: (1) roles and models for communication and transfer of care between primary care physicians and oncologists during active treatment and survivorship; (2) care navigation through designated personnel or telecommunication processes among care team members; (3) treatment summaries and survivorship care plans; and (4) multidisciplinary communication accompanying patient and practice management within the framework of the Chronic Care Model (N=14). We found a medium-sized effect of cancer care coordination on care usage outcomes among the randomized clinical trials (e.g., reduced Emergency Department visits; g = 0.37 [95% CI = 0.29 - 0.44], I2= .000. Fail-safe N = 86). Conclusions: The findings from this current systematic review and meta-analysis will contribute to the evidence base on strategies that can improve the coordination of cancer care, particularly for patients with multiple chronic conditions, and thereby advance the goals of health care reform in the US.
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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 | 0.090 | 0.236 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".