Video Consultations in Primary Care Outside Office Hours
Bibliographic record
Abstract
Background Out-of-hours primary care (OOH-PC) is facing increasing demands and workload with many negative consequences, including longer waiting time and increased risk of treatment delay and safety incidents. During the COVID-19 pandemic, video consultation (VC) was introduced as an alternative to face-to-face contact. We hypothesize that VC contributes to sustainable OOH-PC by changing patient flows, decreasing workload, and reducing waiting time. Objective This study aims to evaluate the use of video in telephone triage in OOH-PC by studying user rate, the effect on contact patterns, and patient characteristics related to receiving a VC. Methods We conducted a register-based study of VC use in OOH-PC, including all Danish residents contacting OOH-PC in the regions of Central Denmark, Southern Denmark, Northern Denmark, and Zealand. The study population will be followed from birth, immigration, or March 1, 2020 (whichever came last), until death, emigration, or December 31, 2021 (whichever comes first). We will use national registers, linking data with the unique personal identification number. We plan to conduct descriptive analyses, calculating the proportion of VC of all teletriage consultations per month during the study period. We plan to use regression models to measure the association between VC and triage outcome and the association between VC and patient characteristics, calculating risk ratios and 95% CIs. Both crude and mutual adjusted risk ratios for patient characteristics will be presented. Results Data analyses started in May 2022. Conclusions A preliminary conclusion will be presented at the conference. Conflicts of Interest None declared.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".