Tailoring and Evaluating Treatment with the Patient-Specific Needs Evaluation: A Patient-Centered Approach
Bibliographic record
Abstract
BACKGROUND: No patient-reported instrument assesses patient-specific information needs, treatment goals, and personal meaningful gain (PMG), a novel construct evaluating individualized, clinically relevant improvement. This study reports the development of the Patient-Specific Needs Evaluation (PSN) and examines its discriminative validity (ie, its ability to distinguish satisfied from dissatisfied patients) and test-retest reliability in patients with hand or wrist conditions. METHODS: A mixed-methods approach was used to develop and validate the PSN, following Consensus-Based Standards for the Selection of Health Measurement Instruments guidelines, including pilot testing, a survey (pilot, n = 223; final PSN, n = 275), cognitive debriefing ( n = 16), expert input, and validation. Discriminative validity was assessed by comparing the satisfaction level of patients who did and did not achieve their PMG ( n = 1985) and test-retest reliability using absolute agreement, the Cohen kappa, and intraclass correlation coefficients ( n = 102). The authors used a sample of 2860 patients to describe responses to the final PSN. RESULTS: The PSN has only 5 questions (completion time, ±3 minutes) and is freely accessible online. The items and response options were considered understandable by 90% to 92% of the end-users and complete by 84% to 89%. The PSN had excellent discriminative validity (Cramer V, 0.48; P < 0.001) and moderate to high test-retest reliability (kappa, 0.46 to 0.68; intraclass correlation coefficients, 0.53 to 0.73). CONCLUSIONS: The PSN is a freely available, patient-centered decision support tool that helps clinicians tailor their consultations to patients' individual needs and goals. It contains the PMG, a novel construct evaluating individualized, clinically relevant treatment outcomes. The PSN may function as a conversation starter, facilitate expectation management, and aid shared decision-making. The PSN is implementation-ready and can be readily adapted to other patient populations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".