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Quality of care in non-small cell lung cancer (NSCLC): Findings from the Florida Initiative for Quality Cancer Care (FIQCC).

2010· article· en· W2265055002 on OpenAlexaboutno aff
Tawee Tanvetyanon, M. Corman, William J. Fulp, J. Lee, Paul B. Jacobsen

Bibliographic record

VenueJournal of Clinical Oncology · 2010
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLung cancerStage (stratigraphy)AuditPsychological interventionQuality ScoreOncologyQuality managementInternal medicineNursing

Abstract

fetched live from OpenAlex

6018 Background: To date, no quality of care indicators (QI) specific for NSCLC are widely accepted. We proposed a set of QI and are reporting on quality of care using data from FIQCC consortium which is comprised of 11 oncology practices. In addition, this study explored the impact of patient volume on quality of care. Methods: Major care guidelines (NCCN, ACCP, ESMO, Ontario CC, ELCWP) were systematically reviewed for potential QI. Survey was conducted among NCCN NSCLC panel in 2008 for QI with strong agreement ratings. Chart abstraction manual was created, structured chart abstraction conducted for all NSCLC patients first seen by oncologist in 2006 in each site, and independent audits performed to ensure accuracy and reliability. Results: 10 NSCLC-specific QI were established. 531 charts were sampled from 11 sites: 4 sites classified as higher volume (median 2006 NSCLC-patient volume = 314; range 244-862) and 7 as lower volume (median = 129; range 90-217). Patient median age was 68 years; 14% stage-I, 6% stage-II, 26% stage-III, and 49% stage-IV/wet IIIB. Performance rates across practices for the QI ranged from 44-91% (Table). Among the lowest rates were 1) practice of brain staging before chemoradiation in stage-III, 2) formal assessment of unresectability in unresected early NSCLC, and 3) performance status assessment in advanced NSCLC. Conclusions: Areas with the greatest potential for improvement in the quality of care for NSCLC were related to nonchemotherapeutic interventions. We found limited evidence for the difference in quality of care based on patient volume. QI Practice rates % (N) Rates in higher-volume/lower-volume sites p values ≥2 N2 stations assessed at surgery 75 (99) 79/90 0.36 Post-op CT scan done by 6 months 64 (106) 68/59 0.41 Adjuvant chemo referral by 8 weeks 89 (54) 86/92 0.67 No adjuvant radiation in stage I, II 91 (70) 91/92 1.00 Unresected early stages, had surgical evaluation 60 (83) 67/56 0.37 Concurrent chemoRT for unresected stage III 90 (102) 91/89 0.75 Brain staging before chemoRT 59 (88) 63/56 0.67 Use standard chemo, early stages 79 (150) 84/75 0.22 Performance status assessed, advanced stages 44 (260) 54/32 < 0.001 Use standard chemo, advanced stages 84 (194) 79/90 0.07 Author Disclosure Employment or Leadership Position Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Pfizer

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)0.007
metaresearch head score (Gemma)0.020
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 score0.094
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.127
GPT teacher head0.522
Teacher spread0.395 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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