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Record W3030290035

Lessons learned from cases of rib fractures after manual therapy: a case series to increase patient safety.

2020· article· en· W3030290035 on OpenAlexaff
Daphne To, Anthony Tibbles, Martha Funabashi

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsCanadian Memorial Chiropractic College
Fundersnot available
KeywordsChiropracticMedicineManual therapySpinal manipulationPhysical therapyAlternative medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify commonalities among cases of rib fractures after spinal manipulative therapy (SMT); discuss chiropractors' case management perspectives; and propose strategies for prevention and/or management of future cases. METHODS: Semi-structured interviews were conducted with chiropractors who identified cases of rib fractures after SMT at a chiropractic institution's teaching clinics. Patient characteristics, incident characteristics, and chiropractors' perspectives were collected and analysed. RESULTS: Three chiropractors were interviewed, each identifying one case. Patient ages ranged from 57-77; two were female; two had osteopenia; two cases involved thoracic SMT; and one involved lumbar SMT. Chiropractors agreed that verifying and updating potential contributing factors for rib fractures, transparent communication prior to SMT and/or after the adverse event (AE) occurrence, and enhancing student education on AE management were important. CONCLUSION: Important lessons can be learned from AEs, despite their infrequent occurrences. A more open and constructive patient safety environment is needed within the chiropractic profession.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

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

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 head0.049
GPT teacher head0.307
Teacher spread0.257 · 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 designOther design
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

Citations10
Published2020
Admission routes1
Has abstractyes

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