Developing recommendations for the diagnosis and treatment of Lyme disease: the role of the patient's perspective in a controversial environment
Bibliographic record
Abstract
CONTEXT: The Ministry of Health in Québec requested the National Institute of Excellence in Health and Social Services to produce clinical and implementation recommendations for the prophylaxis, diagnosis, and treatment of Lyme disease. OBJECTIVES: (i) Describe the process of trialing different modalities of patient engagement as a means to integrate a diversity of patient perspectives and (ii) Describe the learning process of INESSS regarding the integration of the patient perspective. METHODOLOGY: All documents were analyzed, and a survey with all advisory committee members and semi-structured interviews with stakeholders were conducted. Each interview was transcribed verbatim and imported into QDA miner software for the purposes of analysis. Data analysis was carried out concurrently with data collection to allow for an iterative approach between data collection and analysis. RESULTS: Five methods to integrate the perspectives of patients were used: (i) interviews with patients, (ii) inclusion of patient partners within the advisory committee, (iii) literature review, (iv) focus groups with one patient association, and (v) feedback from patient associations on recommendations intended for decision makers and other targeted stakeholders. The patient partners influenced decisions by sharing their experiential knowledge. The patient interviews and the literature review added an in-depth perspective on the disease and experience with the healthcare system. The patient association members shared their perspectives and helped disseminate the recommendation to sustain a practice change. CONCLUSION: The combination of methods to collect and integrate patients' knowledge and patient associations' perspectives helped develop a comprehensive understanding of a controversial object of evaluation.
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 | 0.102 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".