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IMPROVING OSTEOPOROSIS MANAGEMENT IN AT‐RISK FRACTURE CLINIC PATIENTS

2005· letter· en· W1576935381 on OpenAlexaffabout
Maureen C. Ashe, Heather McKay, Patricia A. Janssen, Pierre Guy, Karim M. Khan

Bibliographic record

VenueJournal of the American Geriatrics Society · 2005
Typeletter
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsBC Children's HospitalVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineOsteoporosisPsychological interventionIntervention (counseling)Physical therapyFamily medicinePediatricsInternal medicineNursing

Abstract

fetched live from OpenAlex

To the Editor: We would like to draw your attention to an important but preventable bone health and healthcare delivery dilemma that we believe can be improved with a simple, low-cost intervention. In 1982, researchers at the Mayo Clinic reported that history of a previous low-trauma or fragility fracture was associated with a 1.3 relative risk of subsequent hip fracture1 and this was likely an underestimation of the risk.2-4 In response, national guidelines in the United States and Canada emphasized that low-trauma fractures should not merely be treated orthopedically,5 but should also prompt a family physician evaluation to assess osteoporosis risk and manage it appropriately. Despite this ideal opportunity for secondary prevention of osteoporosis, numerous publications have highlighted the continual low investigation (∼20%) rates after a sentinel fracture.6–9 Even fewer patients received adequate therapy, highlighting the “knowledge-care gap.” In a review of the largest series of wrist fractures to date, only 23% of “at risk” patients were treated for osteoporosis,9 whereas one study10 reported from a retrospective review of a large database that only 9.8% of the women and 2.9% of the men were screened for osteoporosis. Yet another study11 suggests that screening for these “sentinel fractures” would reduce hip fractures 9%. Despite increasing awareness of this problem, few controlled studies have tested interventions to improve diagnosis and management. There have been recent publications that have tested interventions aimed at changing physician practice patterns.12 Recently, a simple four-part intervention using a diagnosis/management questionnaire and counting “osteoporosis best practices” offered by the family physician to patients with a wrist fragility fracture was tested.12 Participants who volunteered for this study were allocated into one of two groups (intervention or control). Each participant in the intervention group received an information sheet explaining that she or he had suffered a low-trauma fracture diagnostic of osteoporosis, a request to take a letter from the orthopedic surgeon who was managing the fracture to show to the family physician alerting her or him to the recent low-trauma fracture, and a follow-up telephone call at 4 to 6 weeks to remind the participant to visit her or his family physician. The fourth element of the intervention was a facsimile from the orthopedic surgeon to the family physician specifically requesting general practitioner assessment and management of osteoporosis. In the control group, participants were not given any additional information on risk of osteoporosis by the researchers. Control group patients received usual care, defined as treatment for the fracture by the hospital staff and routine notification to the family physician of the fracture and any follow-up plans. Both groups were telephoned at 6 months to administer the diagnosis management questionnaire. This questionnaire was developed specifically to ascertain the osteoporosis investigation rate and osteoporosis “best practices” as recommended by the 2002 Osteoporosis Consensus.5 The original distal radius fracture “WristWatch” study was expanded to include humeral fractures. Sixty-two participants were enrolled in this study (36 control and 26 intervention). (See Table 1 for participant characteristics.) In this multicomponent quality-improvement intervention of a patient-education and physician-alerting system, 73% of participants in the intervention group were investigated for osteoporosis. The absolute difference was 54%, and the relative risk was 3.8 (95% confidence interval=1.9–7.6) Once again, investigation for osteoporosis was significantly higher in the intervention group (P<.001). It is tempting to speculate that there might be at least two ways that this study could be translated into clinical practice. Standing orders contained within the orthopedic surgeon's follow-up note to the family physician would ensure that patients and family physicians instigate optimum osteoporosis care. Alternatively, a healthcare worker could liaise between potential patients at risk and the healthcare services to initiate the care pathway (fracture liaison service). One group of providers13 tested such a model in their clinical practice, and a designated team member performed case finding and initiated appropriate investigation. This can be an effective service for larger facilities with adequate funding. Although it can be seen as the criterion standard, there are people and regions who are not able to access such services. Although the current study showed that family physicians investigated at-risk patients for osteoporosis when an orthopedic surgeon prompted them to do so, future studies could investigate the use of clinical pathways that use the expertise of other health professionals (e.g., physical therapist, nurse) at the fracture clinic or emergency department level to systematically initiate family physician follow-up regarding osteoporosis. Osteoporosis is a serious medical condition and at-risk patients need to be identified. A fragility fracture is a sentinel event and can be used to effectively initiate investigation. A simple intervention can influence physician behaviors. Larger studies should further refine this intervention by testing the effectiveness of its individual components.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.012
GPT teacher head0.302
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations8
Published2005
Admission routes2
Has abstractyes

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