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Record W1982755842 · doi:10.3138/ptc.2011-29

A Spinal Triage Programme Delivered by Physiotherapists in Collaboration with Orthopaedic Surgeons

2012· article· en· W1982755842 on OpenAlexaffvenue
Brenna Bath, Bonnie Janzen

Bibliographic record

VenuePhysiotherapy Canada · 2012
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineTriageDescriptive statisticsPhysical therapyHealth careDemographicsFamily medicineMedical emergency

Abstract

fetched live from OpenAlex

PURPOSE: To describe the characteristics of participants in a physiotherapist spinal triage programme, compare the profiles of patients for whom surgery was and was not recommended by a surgeon, and determine the surgical yield among those referred to surgeons. METHODS: Data were collected retrospectively by reviewing charts of people who used the service over a 3-year period (2003-2006). Data from up to1,096 people were used in the analysis; complete data were available for 299 people. Descriptive statistics were used to summarize demographics, clinical features, and management recommendations. Characteristics of those who were and were not recommended for surgery were examined using Pearson's chi-square or Fisher's Exact tests. RESULTS: The majority of 746 participants were classified as "mechanical spine" (92.5%), 2.9% were "other body part," 2.5% were "medical/other," and only 2% were classified as "surgical spine." Recommendations for surgery (by a surgeon) were independent of patients' age, sex, duration of symptoms, residence (urban/rural), source of health care funding, and diagnosis. The surgical yield was 80%. CONCLUSIONS: Most people were not considered candidates for surgery. Triage assessment by physiotherapists can increase the efficiency of an orthopaedic surgeon's caseload by reducing the number of non-surgical referrals and can thus help to ensure more timely access to appropriate health care.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.375
Teacher spread0.355 · 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.

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

Citations47
Published2012
Admission routes2
Has abstractyes

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