Nasopharyngeal Carcinoma Volume Measurements Determined with Computed Tomography: Study of Intraobserver and Interobserver Variability
Bibliographic record
Abstract
OBJECTIVE: To investigate the intraobserver and interobserver variability of computed tomography-based volume measurements of nasopharyngeal carcinoma. DESIGN: Prospective study. SETTING: Tertiary care centre. METHODS: The primary tumour volume of 13 nasopharyngeal carcinomas was repeatedly measured by two trained observers independently in two different sessions, using the summation of area technique. MAIN OUTCOME MEASURES: Mean tumour volume and its standard deviation were calculated for each tumour. Statistical analysis was done with multivariate analysis, linear regression, and a two-way analysis of variance (ANOVA) random effects model. RESULTS: The coefficient of variation was less than 20% in 11 volume measurements, but a large discrepancy between observers was noted in two tumours with involvement of the paranasal sinuses. A good linear correlation was found between mean tumour volume and its standard deviation: standard deviation = 0.26 volume - 2.48 (r = .80). When the two tumours with a large coefficient of variation were excluded, the two-way ANOVA random effects model revealed that both the interobserver (p = .83) and the intraobserver (p = .90) effect are not statistically significant; interobserver variability was the major component of total variability (71.0%). CONCLUSIONS: Total variability in the computed tomography-based measurement of nasopharyngeal carcinoma volume is small by having the measurements done by a trained observer, except in tumours with involvement of the paranasal sinuses.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".