Inter-rater reliability of stress signatures in exfoliated primary dentition - Improving scientific rigor and reproducibility in histological data collection
Bibliographic record
Abstract
Accentuated Lines (ALs) in tooth enamel can reflect metabolic disruptions from physiological or psychological stresses during development. They can therefore serve as a retrospective biomarker of generalized stress exposure in archaeological and clinical research. However, little consensus exists on when ALs are identified and inter-rater reliability is poorly quantified across studies. Here, we sought to address this gap by examining the reliability of accentuated (AL) markings across raters, in terms of both the presence versus absence of ALs and their intensity (HAL= Highly Accentuated, MAL= Mildly Accentuated, RL= Retzius Line). Ratings were made and compared across observers (with different levels of experience) and pairs of raters (who agreed on AL coding through consensus meetings) (N = 15 teeth, eight observers). Results indicated that more experience in AL assessment does not necessarily produce higher reliability between raters. Most disagreements in intensity ratings occurred in categories other than HAL. Furthermore, when AL assessment was performed by pairs of raters, reliability was significantly higher than individual assessments (Gwet's AC1 = 0.28 to 0.56 for line presence assessment; Gwet's AC1 = 0.48 to 0.64 for line intensity assessment). Based on these results, we recommend a workflow called IRRISS (Improving Reliability and Reporting In Scoring of Stress-markers) to increase rigor and reproducibility in histological analysis of dental collections. The introduction of IRRISS is well-timed, given the surge in studies of teeth occurring across anthropological, epidemiological, medical, forensic, and climate research fields.
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 | 0.199 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".