Nurse Practitioner Led Identification and Treatment of Knee Pain Severity Based on Evidence Classification Protocols
Bibliographic record
Abstract
Title: Nurse Practitioner Led Identification and Treatment of Knee Pain Severity Based on Evidence Classification Protocols Background: Knee pain has become the 10th leading office visit in the United States. Prevalence of knee pain has increased 65% in the past 20 years accounting for approximately 4 million clinic visits every year. One out of five men and one out of four women in the United States suffers from knee pain. Treatment protocols in actuality are based on physical therapy, pharmacological treatment, or surgical management. However, research has demonstrated that knee pain and progression of knee related illnesses may be prevented by diet, weight control, knee exercises, and early treatment intervention. Purpose of Project: a) To determine the level of knee pain severity after administration of the Western Ontario and McMaster Universities Arthritis Index (WOMAC) questionnaire; b) To increase knee pain management knowledge by at least 50% in a 2-month period. EBP Model/ Framework: The John Hopkins Model was used to guide this project. Evidence Based Intervention(s): The WOMAC questionnaire was provided to every patient presenting to the clinic with a complaint of knee pain. Printed material on knee pain management and resources were provided based on WOMAC Scores. Evaluation/ Results: A total of eighteen patients received knee pain management educational material. Fourteen respondents expressed an increase in knowledge on how to properly manage knee pain. One respondent expressed no benefit, and three respondents were not able to be reached by phone. Implications on Practice: Early non-surgical interventions may contribute to prevention and a better management of knee pain. Early detection and management will improve quality of life, decrease progression, and decrease clinic visits. Conclusion: The WOMAC instrument is a reliable and validated tool that has been utilized in numerous research trials as an assessment tool for different knee conditions. Implementation of the WOMAC tool on a primary care facility will assist on obtaining specific information related to knee pain to ensure that patients are provided with the most up to date research information.
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 | 0.097 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".