Forskningsnettverk med leger og tannleger
Bibliographic record
Abstract
English summary General practitioners and dentists - a pilot project based on data extraction from electronic patient records. 8-14 Clinical research in primary care is scarce compared to hospital care. In part, this may be due to the lack of infrastructure for such research in a decentralized service. Research institutions in general practice and oral health in Norway have collaborated to establish practice-based research networks (PBRN) in primary care. PBRNs have been successfully established in countries such as the UK, the Netherlands and Canada. The aims of the project were (i) to evaluate experience with recruitment, organization and participation of general practitioners (GPs) and dentists in a pilot research network; (ii) to develop and test methods for extracting anonymous data from GPs' and dentists' electronic patient records (EPR), and (iii) to evaluate GPs' and dentists' routines for handling dry mouth problems and interaction between the two professions in this respect. The results are presented in two papers. The current paper addresses the two first aims. A total of 74 doctors and 77 dentists participated. Altogether, 165 adminitrative hours were used for recruitement of 151 clinicians. The extraction of data caused some problems and the free text extracts required particularly thorough review to ensure validity. More than half of the participants answered a questionnaire. Most of the respondents would consider participation in a future research network, and they suggested courses, financial compensation or support for their own quality assurance activity as important facilitating factors. Scarcity of time and heavy workload were mentioned as barriers. There were small differences between the two professions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.224 | 0.089 |
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".