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Record W2134887124 · doi:10.1093/occmed/51.1.62

Back pain in pre-registration house officers

2001· article· en· W2134887124 on OpenAlexaboutno aff
Joseph de Bono

Bibliographic record

VenueOccupational Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsBack painMedicineLow back painQuarter (Canadian coin)Physical therapyWork (physics)Back injuryPopulationRecallFamily medicinePsychologyAlternative medicineEnvironmental health

Abstract

fetched live from OpenAlex

Back pain is a major burden on the working population. It is a particular problem amongst hospital staff, especially nurses. It has been poorly studied amongst doctors. Pre-registration house officers (PRHOs) starting their careers are exposed to a number of risk factors for back problems, both physical and psychological. This questionnaire-based study investigated the prevalence of back pain and its impact on the work of new graduates from two UK medical schools. Around half of the newly qualified PRHOs had significant back pain, one-quarter at least once a week. The frequency of back pain doubled once they started work, although the overall prevalence remained static. One in 10 of them had been unable to perform their normal work activities at some stage because of back pain. One in eight had sought professional help for back problems in the previous 5 years. Fewer than 50% of newly qualified doctors could recall any formal training in lifting and handling.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.319
Teacher spread0.297 · 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

Citations6
Published2001
Admission routes1
Has abstractyes

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