Coordinating cancer care: Measurement and intervention approaches across the cancer continuum.
Bibliographic record
Abstract
103 Background: According to a landmark study by the Institute of Medicine, patients with cancer often receive poorly coordinated care in multiple settings from many providers. Lack of coordination is associated with poor symptom control, medical errors, and higher costs. The aims of this presentation are to: (1) describe the state-of-the science on cancer care coordination measures and intervention outcomes from a systematic review and meta-analysis of empirical papers published between 1980-2013; (2) explore the implications of these findings from the patient, provider, healthcare system, and national policy perspectives. No similar review has yet been published. Methods: Of 1,241 abstracts collected from a systematic search of PubMed, MeEMBASE, Medline, CINAHL, and Cochrane Library, 50 studies met the inclusion criteria. Each study had US or Canadian participants; comparison or control groups, measures, times, samples, and/or interventions. Two raters independently applied a standardized search strategy and coding scheme. Eight studies (14 outcomes) met the additional criteria for the meta-analysis. We used the Care Coordination Atlas (McDonald, 2010) definition of care coordination. Results: Overall, coordination improved cancer care across 83% (44) of the measured outcomes. Interventions led to more appropriate healthcare use (g = 0.37 [95% CI = 0.29 – 0.46]; I2= 0.00) across screening (patient navigation), treatment (home telehealth, nurse case management and education), and end-of-life care (early palliation). Measures varied considerably in psychometric quality and were limited in focus. They included; rates of guideline compliance (screening), timeliness of care (diagnosis), health-related quality of life (treatment), cancer-related distress (survivorship), and home death (end-of-life). Conclusions: The findings revealed effective interventions across the cancer continuum from screening to end-of-life. More, and better measures are needed across the cancer continuum. We discuss the implications of these findings for more and better measures, approaches to implement effective interventions in clinical settings, and to develop supportive policy (and reimbursement) contexts.
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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 | 0.142 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".