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Record W2222838744

The upcoming epidemic of fragility fractures in Canada.

2003· editorial· en· W2222838744 on OpenAlexaboutno aff
Peter C. O’Brien

Bibliographic record

VenuePubMed · 2003
Typeeditorial
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePolytraumaPopulation ageingPopulationOsteoporosisHip fractureEmergency medicineMedical emergencyEnvironmental healthInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

The population in Canada is aging. In 1997, Statistics Canada recorded that 12% of the population was over 65 years of age and 1% over 85 years, and predicted that by 2041, 25% will be over 65 years of age and 4% will be 85 years or older.1 Age is a major risk factor for low-energy fracture. Over the next few decades a major increase is anticipated in the number of patients with fragility fractures (hip, spine, tibia, humerus, radius) requiring in-hospital treatment for their injuries. Between 1981 and 1995, the number of hip fractures in Canada increased from 17 823 to 27 375. The number has continued to increase and is expected to reach 88 124 by 2041.2 In the elderly, hip fractures can cause considerable morbidity and mortality, consuming large amounts of health care resources. The direct cost in Canada of treating osteoporosis is estimated at $1.3 billion annually.3 In this issue (page 446), Lieberman and associates4 note that the aging population has already affected the delivery of surgical care in Quebec. An ever-increasing number of elderly patients are admitted to level I trauma hospitals with single-limb, low-energy injuries resulting from falls. These patients use expensive in-hospital resources (beds and operating room time) that historically have been used by younger patients with complex polytrauma. To ensure that tertiary and quaternary facilities remain available to those who need them, the authors suggest a change in the way geriatric patients suffering low-energy injuries are triaged to hospitals. The epidemic of geriatric fragility fractures in Canada is beginning. Without thoughtful planning, these patients will use hospital facilities and resources that have been designated for others. Triage of single-limb, low-energy injuries away from level I trauma centres is important, but community level hospitals cannot be expected to care for the large volume of patients. Governments must allocate new funds to care for such patients so that appropriate discharge facilities are available. Many patients now spend longer than necessary in acute care hospitals because access to rehabilitation and appropriate alternative care facilities is inadequate. Canada needs specialized centres designed to provide excellent care to geriatric patients with fractures in a cost-effective way. One possible model would involve designated regional hip fracture care facilities. Ideally, such facilities would be affiliated with an acute care hospital but would function as a separate service. The ratio of nursing, physiotherapy, occupational therapy and social work staff could be adjusted according to the level of care needed; medical, surgical, nursing and rehabilitation expertise also could be specifically targeted. This likely would be more cost efficient and improve patient outcomes. It would also enhance research and educational opportunities. The geriatric fragility fracture epidemic will have a significant impact on health care delivery. Lieberman and colleagues have shown that the time for action is now.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.086
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0040.001
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0370.004

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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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