Physician–patient shared decision making in the treatment of primary immunodeficiency: an interview-based survey of immunologists
Bibliographic record
Abstract
Background: Patient–physician shared decision making (SDM) can result in better care as well as reduced treatment costs. A better understanding of the factors predicting when physicians implement SDM during the treatment of primary immunodeficiency (PID) could provide insight for making recommendations to improve outcomes and reduce healthcare costs in PID and other long-term chronic conditions. Method: This study made use of grounded theory and was based on the interview responses of 15 immunologists in the United States. It focused on their decision making in the diagnosis and treatment of PID, how they interact with patients, and the circumstances under which they encourage SDM with patients. Results: All invited immunologists took part in the interviews and were included in the study. All but one had 10 or more years of experience in treating PID. The study found that SDM is bounded/limited by “nudging” bias, power balance considerations, and consideration of patient health literacy alignment. Immunologists also reported that they were mainly responsible for coordinating care and for allowing sufficient time for consultations. Conclusion: SDM occurs between the physician and patient throughout the treatment of PID. The study also shows the ways physicians influence SDM by guiding patients through the process. Statement of novelty: Little is known about the factors that influence SDM in the long-term management of chronic diseases. The present study investigated the extent to which immunologists experienced in the treatment of patients with PID include SDM in clinical practice. Findings such of these may be of use when formulating treatment guidelines and improving the effectiveness of long-term management of PID.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".