Substantial clinical benefit values demonstrate a high degree of variability when stratified by time and geographic region
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: A Substantial Clinical Benefit (SCB) value is the amount of change in a patient-reported outcome measure required for a patient to feel they significantly improved from an intervention. Previously published SCB values are often cited by researchers when publishing outcomes data. Where these SCB values are set can have a large impact on the conclusions drawn from a study citing them. As such, the goal of this study was to determine the generalizability of SCB values for a procedure when stratified by time from surgery and geographic region. Methods: A nationwide outcomes database was utilized to obtain preoperative, one-year, and two-year postoperative outcome measurements for patients who underwent anatomic total shoulder arthroplasty (TSA) or reverse TSA. The data were divided into three geographic regions: the South, the Midwest, and the West. An East region was not included due to its limited number of patients. SCB values were calculated for four outcomes measures: Single Assessment Numeric Evaluation score, American Shoulder Elbow Surgeons score, Visual Analog Scale, and Western Ontario Osteoarthritis of the Shoulder score. SCB values were calculated for each region, for each procedure, and at both one and two years postoperatively. To determine the variability of potential SCBs within each region, simulated datasets were created to determine a distribution of possible calculated SCBs. Results: A total of 380 anatomic TSA patients and 543 reverse TSA patients were included for analysis. There was a high degree of variability of SCB values when stratified by procedure, time, and region. While some simulated datasets did produce homogenous SCB distributions among regions, some outcome measures demonstrated a large heterogeneity in distribution among regions, with concomitant large distributions of values within individual regions. Conclusions: There is notable heterogeneity of SCB values when stratified by region or time. The current method of citing previously published SCB values for determining the efficacy of an intervention may be inappropriate. It is likely that this variability holds true in other areas of orthopedics.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it