Using electronic knowledge resources for person-centered medicine – I: An evaluation model
Bibliographic record
Abstract
Rationale: Electronic knowledge resources are routinely searched by physicians for clinical information. The value of clinical information for physicians can be conceptualized in accordance with the ‘Acquisition-Cognition-Application’ model from information studies. In previous work, seven reasons for searching for information (acquisition), and 10 cognitive impacts (cognition), were proposed.Aims and objectives: In two companion papers, our objectives are: (1) to propose a person-centered model of the value of clinical information retrieved by health professionals from electronic knowledge resources, called the ‘Acquisition-Cognition-Application-Outcome’ (ACAO) model and (2) to propose a related concept, the Number Needed To Benefit From Information (NNBI), i.e., the number of patients for whom information has to be retrieved to observe health benefits for one patient. Our research questions are as follows. Part 1: What is known about patients health benefits associated with the use of information that physicians retrieve from electronic knowledge resources? Part 2: How do patients benefit when physicians use information from electronic knowledge resources?Methods: This paper (Part 1) reports a mixed studies review of the medical literature on types of information use and subsequent patient health benefits.Results: Twenty-nine papers were included in this review. These papers support four proposed types of information use and four of five types of patient health benefits.Conclusion: This review of the medical literature supports the ACAO model and paves the way toward a new concept for examining patient health benefits associated with information use, the NNBI described in Part 2.
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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 | 0.042 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".