Quality of care in non-small cell lung cancer (NSCLC): Findings from the Florida Initiative for Quality Cancer Care (FIQCC).
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".