Bibliographic record
Abstract
Shared decision making and high blood pressureEvidence-based practice has patients' values and preferences as one of its 3 pillars.To properly elicit this pillar, health professionals need to do shared decision making (SDM).To do SDM, clinicians need to be provided with information that would allow them to discuss with patients the risks of their medical condition, the different treatment options and the potential benefits and harms associated with these treatments.Hopefully, most pharmacists would wish to practise in this way.The decisions around the treatment of high blood pressure (HBP) are an ideal opportunity for SDM.There is solid evidence that some HBP treatments reduce cardiovascular disease (CVD) risk.However, it is important to remember that HBP for the good majority of patients is an asymptomatic risk factor that does not progress to morbidity, and most people, despite a lifetime of treatment, will not derive a CVD benefit. 1 In addition, all HBP pharmacologic treatments are associated with the potential for adverse effects, and all incur an inconvenience and cost that an individual patient would have to balance against the possibility of benefit.In their recent article in the January/February 2018 issue of CPJ, 2 the authors presented what they felt were the update highlights and the important elements from the most recent 2017 Hypertension Canada guidelines. 3 I read their article with the eye of seeing if one could extract information from this update that would help pharmacists engage in SDM.It was disappointing to find that within the 9 pages of this article, there was not a single mention of the following key aspects/treatment information
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 | 0.059 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".