Subcutaneous connective tissue reactions to <scp>iR</scp>oot <scp>SP</scp>, mineral trioxide aggregate (<scp>MTA</scp>) <scp>F</scp>illapex, <scp>D</scp>ia<scp>R</scp>oot <scp>B</scp>io<scp>A</scp>ggregate and <scp>MTA</scp>
Bibliographic record
Abstract
AIM: To evaluate connective tissue reactions to iRoot SP (Innovative Bioceramics, Vancouver, BC, Canada), mineral trioxide aggregate (MTA) Fillapex (FLPX) (Angelus Soluções Odontológicas, Londrina, Brazil), DiaRoot Bioaggregate (DiaDent Group International, Burnaby, BC, Canada) and white MTA (Angelus, Londrina, Brazil) in Wistar rats. METHODOLOGY: A total of 128 dentine tubes filled with the materials and 32 empty tubes (control) were implanted into 32 rats. After 7, 15, 30 and 90 days (n = 8 per period), the animals were euthanized, and the tissues were processed for histological evaluation using haematoxylin-eosin (H&E) and Von Kossa (VK) staining. Observations were made for cellular inflammatory components and the presence of multinucleated giant cells (MNGC), macrophages and tissue necrosis. Data were analysed by Fisher's exact and Kruskal–Wallis tests (P < 0.05). RESULTS: In all experimental periods, MTA FLPX and iRoot SP scored higher than the other groups for the variable macrophages (P < 0.05). After 30- and 90-day experimental periods, MTA FLPX scored higher than the other groups for the variable MNGC (P < 0.05). After 90 days, the only group that exhibited samples with severe inflammatory response was MTA FLPX. VK positivity was observed in areas of necrosis in all groups, except in the control group. CONCLUSIONS: The materials were considered biologically acceptable except MTA FLPX, which remained toxic to subcutaneous tissue even after 90 days.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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; 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".