Enhancing our Understanding of Outcomes and Determinants of Outcomes Following Proximal Humerus Fractures in Adults
Bibliographic record
Abstract
There remains significant debate surrounding the optimal management strategies for proximal humerus fracture (PHF) patients. High quality evidence summarizing and appraising the measurement properties and concepts reflected by outcomes used to evaluate recovery, and predictors of outcomes following PHF is needed. Objectives: To summarize and appraise 1) outcome measures; and 2) predictors of outcomes following PHF; and 3) develop novel risk tools to predict subsequent surgery following PHF. Methods: I performed four systematic reviews to discern 1) the measurement properties of outcomes; 2) identify the concepts reflected by these outcomes; and 3) summarize and appraise predictors of i) patient-reported functional disability, and ii) subsequent surgery following initialtreatment. I used these data to develop a population-based cohort of PHF patients, and novel risk tools to predict subsequent surgery following initial treatment. Results: The Disabilities of the Arm, Shoulder and Hand (DASH), Shoulder Function Index, and Euro-Quol-5D scores appear to have the best measurement properties to evaluate PHF patients.The DASH, American Shoulder and Elbow Surgeon’s, and Oxford Shoulder Scores appropriately address limitations in daily activities. While evidence surrounding predictors of outcomes was somewhat conflicting, there were trends toward worse outcomes in patients who were older (primarily in those treated with fixation), injured their dominant arm, and whosefracture had a varus fracture neck shaft angle, or a disrupted medial hinge. My risk tools identified the use of a bone graft, and fixation with a nail or wire (vs. a plate) in patients treated with fixation, poor bone quality in patients treated with replacement, and younger age, as well asdischarge to a destination other than home in patients treated non-surgically as predictors of subsequent surgery. The tools showed good to strong discriminative ability for patients treated with fixation and non-surgically. Conclusion: This thesis generated important new knowledge surrounding the outcomes and predictors of outcomes following PHF. My outcome studies can help clinicians choose measures for evaluating recovery, and for future research. My systematic reviews to identify predictors of outcomes can help select variables potentially associated with poor outcomes. My risk tools can identify patients at risk for subsequent surgery.
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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 | 0.027 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".