Spectrum bias, a common unrecognised issue in orthopaedic agreement studies
Bibliographic record
Abstract
OBJECTIVES: Current studies on the additional benefit of using computed tomography (CT) in order to evaluate the surgeons' agreement on treatment plans for fracture are inconsistent. This inconsistency can be explained by a methodological phenomenon called 'spectrum bias', defined as the bias inherent when investigators choose a population lacking therapeutic uncertainty for evaluation. The aim of the study is to determine the influence of spectrum bias on the intra-observer agreement of treatment plans for fractures of the distal radius. METHODS: Four surgeons evaluated 51 patients with displaced fractures of the distal radius at four time points: T1 and T2: conventional radiographs; T3 and T4: radiographs and additional CT scan (radiograph and CT). Choice of treatment plan (operative or non-operative) and therapeutic certainty (five-point scale: very uncertain to very certain) were rated. To determine the influence of spectrum bias, the intra-observer agreement was analysed, using Kappa statistics, for each degree of therapeutic certainty. RESULTS: In cases with high therapeutic certainty, intra-observer agreement based on radiograph was almost perfect (0.86 to 0.90), but decreased to moderate based on a radiograph and CT (0.47 to 0.60). In cases with high therapeutic uncertainty, intra-observer agreement was slight at best (-0.12 to 0.19), but increased to moderate based on the radiograph and CT (0.56 to 0.57). CONCLUSION: Spectrum bias influenced the outcome of this agreement study on treatment plans. An additional CT scan improves the intra-observer agreement on treatment plans for a fracture of the distal radius only when there is therapeutic uncertainty. Reporting and analysing intra-observer agreement based on the surgeon's level of certainty is an appropriate method to minimise spectrum bias. Cite this article: Bone Joint Res 2015;4:190-194.
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 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 | 0.366 | 0.654 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".