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Record W4321003536 · doi:10.1186/s12998-023-00477-1

Ten years of online incident reporting and learning using CPiRLS: implications for improved patient safety

2023· article· en· W4321003536 on OpenAlexaboutno aff
Mark Thomas, Gabrielle Swait, Rob Finch

Bibliographic record

VenueChiropractic & Manual Therapies · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsnot available
Fundersnot available
KeywordsChiropracticMedicinePatient safetyDescriptive statisticsEuropean unionDistressHealth careFamily medicineMedical emergencyPhysical therapyAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Safety incident (SI) reporting and learning via incident reporting systems (IRSs) is used to identify areas for patient safety improvement. The chiropractic patient incident reporting and learning system (CPiRLS) is an online IRS that was launched in the UK in 2009 and, from time to time, has been licensed for use by the national members of the European Chiropractors' Union (ECU), members of Chiropractic Australia and a Canada-based research group. The primary aim of this project was to analyse the SIs submitted to CPiRLS over a 10-year period to identify key areas for patient safety improvement. METHOD: All SIs reported to CPiRLS between April 2009 and March 2019 were extracted and analysed. Descriptive statistics were used to describe: (1) the frequency of SI reporting and learning by the chiropractic profession, and (2) the character of reported SIs. Key areas for patient safety improvement were developed following a mixed methods approach. RESULTS: A total of 268 SIs were recorded on the database over the 10-year period, 85% of which originated from the UK. Evidence of learning was documented in 143 (53.4%) SIs. The largest subcategory of SIs related to post-treatment distress or pain (n = 71, 26.5%). Seven key areas for patient improvement were developed including: (1) patient trip/fall, (2) post treatment distress/pain, (3) negative effects during treatment, (4) significant post-treatment effects, (5) syncope, (6) failure to recognize serious pathology, and (7) continuity of care. CONCLUSION: The low number of SIs reported over a 10-year period suggests significant under-reporting, however, an upward trend was identified over the 10-year period. Several key areas for patient safety improvement have been identified for dissemination to the chiropractic profession. Improved reporting practice needs to be facilitated to improve the value and validity of reporting data. CPiRLS is important in identifying key areas for patient safety improvement.

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)0.030
metaresearch head score (Gemma)0.103
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 score0.030
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.103
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.153
GPT teacher head0.466
Teacher spread0.313 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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