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Record W2115082732 · doi:10.1186/1471-2474-11-241

Low back pain in junior Australian Rules football: a cross-sectional survey of elite juniors, non-elite juniors and non-football playing controls

2010· article· en· W2115082732 on OpenAlexaboutno aff
Wayne Hoskins, Henry Pollard, Chris Daff, Andrew Odell, Peter Garbutt, Andrew McHardy, Kate Hardy, George Dragasevic

Post-publication record

NatureRetraction
ReasonLack of Approval from Company/Institution;Lack of IRB/IACUC Approval and/or Compliance;
Date7/13/2011 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsFootballMedicineElitePhysical therapyLow back painSports medicineEpidemiologyCross-sectional studyInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Low back pain in junior Australian Rules footballers has not been investigated despite findings that back pain is more prevalent, severe and frequent in senior footballers than non-athletic controls and findings that adolescent back pain is a strong predictor for adult back pain. The aim of this study was to determine the prevalence, intensity, quality and frequency of low back pain in junior Australian Rules footballers and a control group and to compare this data between groups. METHODS: A cross-sectional survey of male non-elite junior (n = 60) and elite junior players (n = 102) was conducted along with a convenience sample of non-footballers (school children) (n = 100). Subjects completed a self-reported questionnaire on low back pain incorporating the Quadruple Visual Analogue Scale and McGill Pain Questionnaire (short form), along with additional questions adapted from an Australian epidemiological study. Linear Mixed Model (Residual Maximum Likelihood) methods were used to compare differences between groups. Log-linear models were used in the analysis of contingency tables. RESULTS: For current, average and best low back pain levels, elite junior players had higher pain levels (p < 0.001), with no difference noted between non-elite juniors and controls for average and best low back pain. For low back pain at worst, there were significant differences in the mean pain scores. The difference between elite juniors and non-elite juniors (p = 0.040) and between elite juniors and controls (p < 0.001) was significant, but not between non-elite juniors and controls. The chance of suffering low back pain increases from 45% for controls, through 55% for non-elite juniors to 66.7% for elite juniors. The chance that a pain sufferer experiences chronic pain is 16% for controls and 41% for non-elite junior and elite junior players. Elite junior players experienced low back pain more frequently (p = 0.002), with no difference in frequency noted between non-elite juniors and controls. Over 25% of elite junior and non-elite junior players reported that back pain impacted their performance some of the time or greater. CONCLUSIONS: This study demonstrated that when compared with non-elite junior players and non-footballers of a similar age, elite junior players experience back pain more severely and frequently and have higher prevalence and chronicity rates.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.033
GPT teacher head0.383
Teacher spread0.351 · 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 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

Citations11
Published2010
Admission routes1
Has abstractyes

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