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Record W4366179088 · doi:10.1186/s12885-023-10835-0

Symptom management care pathway adaptation process and specific adaptation decisions

2023· article· en· W4366179088 on OpenAlexafffund
Emily Vettese, Farha Sherani, Allison A. King, Lolie C. Yu, Catherine Aftandilian, Christina Baggott, Vibhuti Agarwal, Ramamoorthy Nagasubramanian, Kara M. Kelly, David R. Freyer, Etan Orgel, Scott M. Bradfield, Wade Kyono, Michael Roth, Lisa M. Klesges, Melissa Beauchemin, Allison Grimes, George Tomlinson, L. Lee Dupuis, Lillian Sung

Bibliographic record

VenueBMC Cancer · 2023
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversity of TorontoToronto General HospitalSickKids FoundationHospital for Sick Children
FundersNational Cancer InstituteCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsAdaptation (eye)MedicineClinical pathwayCare pathwayPsychologyNursingHealth carePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: There is substantial heterogeneity in symptom management provided to pediatric patients with cancer. The primary objective was to describe the adaptation process and specific adaptation decisions related to symptom management care pathways based on clinical practice guidelines. The secondary objective evaluated if institutional factors were associated with adaptation decisions. METHODS: Fourteen previously developed symptom management care pathway templates were reviewed by an institutional adaptation team composed of two clinicians at each of 10 institutions. They worked through each statement for all care pathway templates sequentially. The institutional adaptation team made the decision to adopt, adapt or reject each statement, resulting in institution-specific symptom management care pathway drafts. Institutional adaption teams distributed the 14 care pathway drafts to their respective teams; their feedback led to care pathway modifications. RESULTS: Initial care pathway adaptation decision making was completed over a median of 4.2 (interquartile range 2.0-5.3) weeks per institution. Across all institutions and among 1350 statements, 551 (40.8%) were adopted, 657 (48.7%) were adapted, 86 (6.4%) were rejected and 56 (4.1%) were no longer applicable because of a previous decision. Most commonly, the reason for rejection was not agreeing with the statement (70/86, 81.4%). Institutional-level factors were not significantly associated with statement rejection. CONCLUSIONS: Acceptability of the 14 care pathways was evident by most statements being adopted or adapted. The adaptation process was accomplished over a relatively short timeframe. Future work should focus on evaluation of care pathway compliance and determination of the impact of care pathway-consistent care on patient outcomes. TRIAL REGISTRATION: clinicaltrials.gov, NCT04614662. Registered 04/11/2020, https://clinicaltrials.gov/ct2/show/NCT04614662?term=NCT04614662&draw=2&rank=1 .

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 score0.964
Threshold uncertainty score0.411

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 head0.313
GPT teacher head0.479
Teacher spread0.165 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

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