The Art and Science of Consultations in Bovine Medicine: Use of Modified Calgary – Cambridge Guides, Part 2
Bibliographic record
Abstract
Abstract This article, part 2 of a 2-part series, describes the next two steps in the application of the Modified Calgary-Cambridge Guides (MCCG) to consultations in bovine medicine, ‘explanation and planning’, and ‘closing the consultation’, and introduces concepts that are associated with all the components of the guide, ‘building the relationship with the client’ and ‘providing structure to the consultation’. Part 1 introduced the aim and framework of the MCCG which enables the practitioner to gain an insight into the client’s understanding of the problem, including understanding aetiology, epidemiology and pathophysiology. Part 2 introduces the framework that provides the opportunity to understand the client’s expectations regarding the outcome, their motivation and willingness to adhere to recommendations. It also describes how to engage and acknowledge the client as an important part of the decision-making process, how to establish responsibilities of both the client and practitioner, and how to reach out to the client at the conclusion of the consultation to make certain that the client’s expectations were met.
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 | 0.027 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".