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Record W2345912100 · doi:10.2106/jbjs.15.01437

Rethinking Orthopaedic Decision-Making for Frail Patients with Hip Fracture

2016· letter· en· W2345912100 on OpenAlexaboutno aff
Robert L Kane, Julie A. Switzer, Mary Forte

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2016
Typeletter
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
Fundersnot available
KeywordsDeliriumHip fractureMedicinePerioperativeCognitionGeriatricsCognitive impairmentPhysical therapyIntensive care medicineSurgeryOsteoporosisPsychiatry

Abstract

fetched live from OpenAlex

Commentary There are substantial research knowledge gaps regarding orthopaedic outcomes after surgical procedures to treat hip fractures in vulnerable elderly patients1. The article by Heng et al. serves to remind us that treating hip fractures must be done thoughtfully. Given the risks related to surgical hip fracture treatment, attention should be paid to treatment decisions for subgroups of high-risk patients. Not all older patients are equally likely to benefit from standard orthopaedic care. Some older adults are at high risk for inpatient hospital complications such as delirium, particularly those with prefracture cognitive impairment. Geriatric hip fractures are associated with high morbidity, mortality, and prolonged functional impairment. At least one-third of patients die within 1 year after a hip fracture2 and less than one-half ever regain their prefracture level of function3. Given this trajectory, efforts such as cognitive screening and other measures of frailty can provide useful insights in planning treatment. Cognitive screening can serve several important roles. It can help surgeons to understand a patient’s ability to understand his or her diagnosis and to be a partner in decision-making. It can also inform surgical decision-making as to the expected demands that each patient may place on a newly stabilized fracture, given the patient’s perceived ability to comply with postoperative functional recommendations. As shown in this article by Heng et al., cognitive status can predict complications, especially perioperative delirium, which, in turn, is a harbinger of increased morbidity and mortality. Patients at high risk should be closely screened preoperatively and postoperatively using programs such as the Hospital Elder Life Program (HELP) for Prevention of Delirium, a multicomponent intervention to prevent delirium in hospitalized older patients. Tools such as the Confusion Assessment Method (CAM) are available in short and long versions. The current study by Heng et al. was conducted in two hospitals that employed a joint practice team with active participation of geriatricians to assist in the care management. However, such resources are not widely available. Less well-served orthopaedic programs and sites must determine ways to routinely involve their medical colleagues in postoperative management and, potentially, preoperative decision-making, particularly in the management of frail patients with hip fracture. Impaired cognition should also signal a need to consider what kind of fracture and rehabilitative treatment is best. The Mini-Cog is a simple screening test. It should be followed up with a more complete cognitive evaluation to assess the level of cognitive impairment. Patients with hip fracture and dementia and/or those admitted from nursing homes have particularly poor medical outcomes and high mortality. More than 25% of elderly patients with hip fracture in the United States have dementia and 20% are admitted from nursing homes4; and these proportions are expected to increase as the population ages. Both patient groups were recently identified as hip fracture outcomes research priorities by a panel of U.S. and Canadian hip fracture experts1. Patients with hip fracture and dementia have high mortality and poor medical outcomes compared with cognitively intact patients, although the mechanisms that account for their worse medical outcomes remain largely unknown. Patients admitted with hip fractures from nursing homes have double the early mortality of non-nursing home patients, approaching 25% by the third month postoperatively4. Hip fracture is associated with excess mortality, even after taking into account prefracture health status, genetic factors, preexisting comorbidities, and lifestyle factors. The highest excess mortality risk seems to be within the first 6 months to 1 year post-fracture, with some variation by age and prior health status, although some excess mortality persists beyond that timeframe2. There is, of course, the potential for confounding relationships5. Preexisting factors such as sex, age, frailty, comorbidity, dementia, and osteoporosis may be associated with fractures themselves6, overall and post-fracture mortality7, and related problems such as falls. Given the high risk that patients from nursing homes and those with dementia may represent, more attention should be paid to what type of treatment is most beneficial. In what instances should surgical treatment be avoided? Several investigators have described outcomes of nonoperative treatment of hip fractures in the elderly. Given the physiologic stress of surgical fixation of a hip fracture and the increasing number of extremely frail individuals who will sustain these and other fragility fractures, questions regarding the wisdom of operative treatment for these patients remain. Patients with impacted femoral neck fractures, patients who are extremely frail, and perhaps even patients who are living in nursing homes when they sustain their fractures may benefit from nonoperative treatment. Focused work regarding goals of care in frail elderly patients and the ability of operative treatment to meet those goals is warranted.

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)0.017
metaresearch head score (Gemma)0.231
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.023
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.231
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.002
Science and technology studies0.0030.005
Scholarly communication0.0050.007
Open science0.0060.003
Research integrity0.0230.026
Insufficient payload (model declined to judge)0.0140.004

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 head0.020
GPT teacher head0.251
Teacher spread0.232 · 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
GenreCommentary

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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Citations2
Published2016
Admission routes1
Has abstractyes

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