What’s in your toolkit? Guiding our patients through their shared decision-making
Bibliographic record
Abstract
n this edition of the Canadian Urological Association Journal, there exists an underlying theme regarding the informational needs of patients, including those requiring a metabolic workup for stones and those facing different treatment preferences for localized prostate cancer.Although it seems self-evident, our patients need and deserve timely, accurate information to make decisions around their diagnostic and treatment options.Failure to provide sufficient information is the most frequent source of patient dissatisfaction and decision regret.Yet the art and science of developing and providing health information that can be optimally used is far from perfect.Despite the amount of available resources and time we spend counselling our patients with prostate cancer, the article by Feldman-Steward et al suggests that almost one-quarter of all prostate cancer patients surveyed wanted more help than they received in making a treatment decision. 1Of those who desired more help, roughly half reported not feeling well-informed.One might suppose that the optics of this implication depend on whether you see the glass "half-full" or "half-empty."The fact that 52% of those who wanted more help making a decision still felt well-informed suggests that additional information may not aid in the decision-making process for these specific individuals.That in and of itself may suggest to some that all the counselling in the world may not be sufficient, and these patients are simply waiting for a divine epiphany to help guide their decisionmaking process.However, to others this may suggest half these patients do require more information and half these patients require different information.Prostate cancer care is complex and patients may have difficulty interpreting risks and benefits.These are frequently single-event probabilities of binary outcomes, provided to patients in the form of estimated risk ratios.Fundamentally, understanding this concept may be an issue with patient numeracy and health literacy, rather than in deficits in the quantity of information provided.Nonetheless, if we measure the quality of our treatment discussion by a patient's ability to understand the encounter, then we provide patients the same quality of care by discussing with them risk ratios they may not understand as we do by sitting silently across from them and ignoring their questions.Thus, it is not surprising that limited health literacy is associated with a preference for physician-directed as opposed to patient-directed treatment decisions. 2n an era where we are striving toward increased patient-centred care, it behooves us to ensure that patients understand the information we are attempting to convey.There is evidence that decision aids improve patient knowledge, reduce decisional regret, improve patient understanding of risk perception, and lead to patients taking on a more active role in their treatment decisions. 3If we cannot provide patients the information they require in a format they understand, or at least the resources to acquire that information, then patients may be forced to look to "Dr.Google" and less reputable sources.In this same issue of the CUAJ, Kobes et al documented the questionable reliability of many of the prostate cancer websites that patients may look to for their informational needs. 4Only 27% of the online websites surveyed identified the author of the information on the site.Furthermore, 60% of websites did not contain a list of references and only 25% contained two or more reliable references.These data indicate that patients are placed in the middle of a difficult conundrum, where they may not understand the information their urologist provides them, but the information they seek out to clarify these discussions may be of questionable quality.High-quality decisions aids have been suggested as a solution to help remedy this challenge.However, before we declare that all patients must be provided diseasespecific decision aids individualized to a numeracy and literacy level congruent with that particular patient, it may surprise some to learn then that decision aids themselves may be subject to the same flaws as the websites surveyed by Kobes et al.In an article
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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".