From “surgical gatekeepers” to “patient navigators”
Bibliographic record
Abstract
Background: While total joint replacement (TJR) is reserved for end-stage osteoarthritis (OA), a greater proportion of OA sufferers require nonsurgical treatment. Given the importance of primary healthcare physicians (PHCPs) in managing patients with OA, we surveyed local PHCPs to ascertain their perceptions and practice patterns regarding hip and knee OA management. Methods: A PHCP survey was developed to determine OA management practices within our region. Responses were analyzed using descriptive statistics. Response variance by practice duration (<15 yr compared with ≥15 yr) was explored. Results: A total of 265 (34%) PHCP surveys were received. PHCPs in practice fewer than 15 yr had a significantly greater proportion of patients with hip and knee OA than those in practice for longer than 15 yr (22% compared with 17%, P<0.0001). PHCPs agreed that radiographs are crucial in the decision for specialist referral (7/10), placing a higher value or utility on nonweightbearing radiographs for a knee OA diagnosis (87%). Mean comfort with discussing TJR indications and contraindications was 6/10 and was slightly higher than discussing postoperative-TJR course (5.7/10). Top treatments were weight loss, low-impact exercise, and oral nonsteroidal antiinflammatory drugs, with PHCPs in practice for fewer than 15 yr assigning higher value or utility to more holistic treatments. Conclusions: There is discordance in the conservative management of hip and knee OA, variability in nonsurgical management comfort level, and a knowledge gap in value and utility of plain radiographs within OA management algorithms among PHCPS. While younger PHCPs are in favor of earlier intervention and practice a more holistic symptom management approach, senior PHCPs stressed greater importance of traditional treatment. With the role of the PHCP shifting from surgical gatekeeper to patient navigator, future efforts aimed at helping facilitate this role are needed.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".