MétaCan
Menu
Back to cohort
Record W2127628566 · doi:10.7205/milmed.172.8.829

The Lessons Learned from the Canadian Forces Physiotherapy Experience during the Peacekeeping Operations in Bosnia

2007· article· en· W2127628566 on OpenAlexaffabout
Luc J. Hébert, Peter Rowe

Bibliographic record

VenueMilitary Medicine · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversité LavalCanadian Armed Forces
Fundersnot available
KeywordsMedicineAnklePhysical therapyPeacekeepingLumbar spineIncidence (geometry)Military medicineLumbarSurgeryPolitical sciencePublic administration

Abstract

fetched live from OpenAlex

The musculoskeletal injuries and soldiers' demographic profiles observed by physiotherapy (PT) officers during the Canadian Forces peacekeeping mission Op-Palladium in Bosnia between 2000 and 2004 were characterized. The number of PT visits (N = 4,167; range, 310-974) and gender distribution (N = 2,558 cases; male, 80.8%-91%; female, 9.0%-16.4%) varied between tours. On average, >30% of the entire Canadian Forces contingent required PT services. Lower limb injuries were the single leading reason for PT treatment (41.8%) followed by the spine (28.5%) and the upper limb (21.5%). The most commonly affected joints were the knee (17.2%) and ankle (16.1%), the shoulder (14.4%), and the lumbar spine (14.4%). The 26 to 35 age group and combat arms showed the highest incidence of musculoskeletal injuries (p < 0.001). The majority of cases seen were subacute and chronic (68%). Primary prevention activities and the capacity to provide the full scope of PT services were identified as two key factors contributing to the maintenance of operational readiness of the troops.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.109
GPT teacher head0.486
Teacher spread0.377 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueMilitary MedicineSame topicOccupational Health and PerformanceFrench-language works237,207