Patient Factors in Referral Choice for Total Joint Replacement Surgery
Bibliographic record
Abstract
BACKGROUND: Although the option of next available surgeon can be found on surgeon referral forms for total joint replacement surgery, its selection varies across surgical practices. OBJECTIVES: Objectives are to assess the determinants of (a) a patient's request for a particular surgeon; and (b) the actual referral to a specific versus the next available surgeon. METHODS: Questionnaires were mailed to 306 consecutive patients referred to orthopedic surgeons. We assessed quality of life (Oxford Hip and Knee scores, Short Form-12, EuroQol 5D, Pain Visual Analogue Scale), referral experience, and the importance of surgeon choice, surgeon reputation, and wait time. We used logistic regression to build models for the 2 objectives. RESULTS: We obtained 176 respondents (response rate, 58%), 60% female, 65% knee patients, mean age of 65 years, with no significant differences between responders versus nonresponders. Forty-three percent requested a particular surgeon. Seventy-one percent were referred to a specific surgeon. Patients who rated surgeon choice as very/extremely important [adjusted odds ratio (OR), 6.54; 95% confidence interval (CI), 2.57-16.64] and with household incomes of $90,000+ versus <$30,000 (OR, 5.74; 95% CI, 1.56-21.03) were more likely to request a particular surgeon. Hip patients (OR, 3.03; 95% CI, 1.18-7.78), better Physical Component Summary-12 (OR, 1.29; 95% CI, 1.02-1.63), and patients who rated surgeon choice as very/extremely important (OR, 3.88; 95% CI, 1.56-9.70) were more likely to be referred to a specific surgeon. CONCLUSIONS: Most patients want some choice in the referral decision. Providing sufficient information is important, so that patients are aware of their choices and can make an informed choice. Some patients prefer a particular surgeon despite longer wait times.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".