Consultation Recording: What Is the Added Value for Patients Aged 50 Years and Over? A Systematic Review
Bibliographic record
Abstract
This systematic review aimed to provide medical professionals with insight into beneficial and harmful effects of consultation recording for patients aged 50 years and over. This insight could enable medical professionals to decide on whether or not to promote consultation recording in their practice. The systematic literature search was performed in six databases; additional relevant articles were sought using the snowball method. Studies were included that investigated the value of consultation recording for patients aged 50 years and over. The selected studies were analyzed on affective cognitive outcomes, behavioral outcomes, and health outcomes. Twenty-five studies of both qualitative and quantitative design were included. Consultation recordings mainly improved patient satisfaction, recall, fulfillment of information needs, and decision-making. Both positive and negative effects were reported on anxiety. The recordings did not distinctly affect functional outcomes or quality of life. In conclusion, consultation recording positively influenced patients' affective cognitive and behavioral outcomes, and the negative effects of consultation recording were minor. Because of the positive effects of consultation replay, we recommend that doctors promote consultation recording among their patients of 50 years and over. However, more studies are necessary among older patients because this patient population is underrepresented in the current literature.
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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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".