Primary medical care continuity and patient mortality: a systematic review
Bibliographic record
Abstract
BACKGROUND: A 2018 review into continuity of care with doctors in primary and secondary care concluded that mortality rates are lower with higher continuity of care. AIM: This association was studied further to elucidate its strength and how causative mechanisms may work, specifically in the field of primary medical care. DESIGN AND SETTING: Systematic review of studies published in English or French from database and source inception to July 2019. METHOD: Original empirical quantitative studies of any design were included, from MEDLINE, Embase, PsycINFO, OpenGrey, and the library catalogue of the New York Academy of Medicine for unpublished studies. Selected studies included patients who were seen wholly or mostly in primary care settings, and quantifiable measures of continuity and mortality. RESULTS: Thirteen quantitative studies were identified that included either cross-sectional or retrospective cohorts with variable periods of follow-up. Twelve of these measured the effect on all-cause mortality; a statistically significant protective effect of greater care continuity was found in nine, absent in two, and in one effects ranged from increased to decreased mortality depending on the continuity measure. The remaining study found a protective association for coronary heart disease mortality. Improved clinical responsibility, physician knowledge, and patient trust were suggested as causative mechanisms, although these were not investigated. CONCLUSION: This review adds reduced mortality to the demonstrated benefits of there being better continuity in primary care for patients. Some patients may benefit more than others. Further studies should seek to elucidate mechanisms and those patients who are likely to benefit most. Despite mounting evidence of its broad benefit to patients, relationship continuity in primary care is in decline - decisive action is required from policymakers and practitioners to counter this.
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".